Psychosocial Safety Climate Scale

The Psychosocial Safety Climate Scale (PSC-12) measures employees' perceptions of organizational commitment to protecting worker psychological health and preventing psychosocial hazards (stress, harassment, bullying). Developed by Dollard and Karasek, and refined by Bailey and colleagues, the PSC-12 captures four dimensions of management support, communication, and hazard prevention. The scale is predictive of workplace stress, burnout, mental health disorders, and absenteeism, making it a leading indicator for organizational health and a lever for preventive intervention.

Bailey, T. S., Dollard, M. F., McLinton, S. S., & Richards, P. A. (2015). Psychosocial safety climate: Latent profiles in Australian workplaces and psychosocial hazard exposure. Int J Stress Manag, 22(4), 413–442.

Dollard, M. F., & Karasek, R. A. (2010). Building psychosocial safety climate: Evaluating the neighbor effect of a safety literature based job stress intervention in two police stations. J Occup Organ Psychol, 83(1), 123–141.


Psychrometric Analysis

Psychrometric Analysis for Humid Air Systems

Psychrometric analysis is the study of humid air (air-water vapor mixtures) and its properties. It is essential for designing and analyzing air conditioning, ventilation, and dehumidification systems. Psychrometric analysis relates dry-bulb temperature, wet-bulb temperature, dew point, relative humidity, and specific humidity through carefully constructed charts and correlations.

ASHRAE. (2021). Handbook-Fundamentals. American Society of Heating, Refrigerating and Air-Conditioning Engineers.

Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.


Public Service Motivation Scale

The Public Service Motivation Scale (PSMS), developed by Perry (1996) and refined by Kim et al. (2013), measures the intrinsic motivation of public sector employees to serve the public interest, contribute to civic good, feel compassion for others, and make self-sacrifices for collective benefit. Public service motivation (PSM) is a defining characteristic of effective public administration, predicting job satisfaction, organizational commitment, performance, and willingness to engage in prosocial behaviors. Essential for public sector recruitment, retention, and culture assessment in government agencies, tourism authorities, and civic institutions.

Perry, J. L. (1996). Measuring public service motivation: An assessment of construct reliability and validity. Journal of Public Administration Research and Theory, 6(1), 5-22.

Kim, S., Vandenabeele, W., Brady, L., Madison, S., Pandey, S., & Ferris, E. (2013). Investigating work motivation across cultures: Development and validation of the multidimensional work motivation scale (MWMS). Public Administration Review, 73(1), 71-81.

Caillier, J. G. (2016). Does public service motivation matter in all contexts? Detailing the structural differences on the relationship between PSM and job satisfaction for public and private sector workers. Public Personnel Management, 45(2), 146-167.

Vandenabeele, W. (2007). Toward a public administration theory of public service motivation: An institutional approach. Public Management Review, 9(4), 545-556.

e-government-adoption-scale citizen-satisfaction-survey tourist-satisfaction-scale tourist-loyalty-scale overtourism-perception-scale


Publication Bias Analysis

Publication Bias Analysis (Egger Test, Funnel Plot)

Small-Study Effects Test
Funnel Plot Asymmetry Test
Egger Regression Test
Yayın Yanlılığı Analizi

Publication bias analysis examines whether the set of studies included in a meta-analysis is a representative sample of all conducted research, or whether studies with non-significant or unfavorable results have been systematically suppressed. Matthias Egger and colleagues introduced the regression-based funnel plot asymmetry test in 1997, providing a formal statistical complement to the graphical funnel plot inspection long used in evidence synthesis.

t-statistic from weighted linear regression

No funnel plot asymmetry (no publication bias)

Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.


Publication Bias

Publication Bias and Selective Outcome Reporting in Research Literature

file drawer problem
selective reporting
outcome reporting bias
funnel plot asymmetry

Publication bias occurs when the results of a study influence whether the study is published. Typically, studies with statistically significant or positive results are more likely to be published than studies with non-significant or negative results, even if both are scientifically valid. This bias distorts the published literature, making treatments appear more effective than they actually are. Rosenthal (1979) termed this the 'file drawer problem': research with null results sits in file drawers, unpublished, creating a biased sample of published evidence. Funnel plots and statistical tests (e.g., Egger test) can detect asymmetry suggesting publication bias; meta-analyses must account for this bias.

Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641.

Chan, A. W., Hrobjartsson, A., Haahr, M. T., Gøtzsche, P. C., & Altman, D. G. (2004). Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA, 291(20), 2457–2465.

p-value-significance multiple-comparisons-problem null-hypothesis effect-size


PubMed and MEDLINE

National Library of Medicine (NLM), U.S. National Institutes of Health

PubMed is a free, publicly accessible literature database maintained by the National Library of Medicine (NLM), a division of the U.S. National Institutes of Health. It provides access to biomedical and life sciences literature from MEDLINE (the curated subset of ~30 million indexed journal articles), life science journals, in-process articles, and preprints. MEDLINE, established in 1966, is the gold standard for biomedical literature indexing, using MeSH (Medical Subject Headings), a hierarchical controlled vocabulary of ~33,000 terms. PubMed is the primary discovery tool for clinicians, researchers, and healthcare professionals worldwide seeking evidence-based information.

National Library of Medicine. (2024). PubMed: Home. Retrieved from https://pubmed.ncbi.nlm.nih.gov/

National Library of Medicine. (2023). MEDLINE Overview. Retrieved from https://www.nlm.nih.gov/medline/medline_overview.html

Landau, H. G. (2005). A revision of the MEDLINE database. NLM Technical Report.

web-of-science scopus-database h-index journal-citation-reports doaj-directory


Pulsar Timing Array

Pulsar Timing Array for Gravitational Wave Detection

PTA
Millisecond Pulsar Timing
Pulsar Timing Residuals

A pulsar timing array uses multiple millisecond pulsars as a distributed network of gravitational wave detectors across the galaxy. Proposed theoretically by Stephen Detweiler in 1979, this method exploits the extraordinary timing precision of pulsars to detect the subtle spacetime distortions caused by gravitational waves. In 2023, the first evidence for a stochastic background of gravitational waves was announced using pulsar timing arrays.

Sazhin, M. V. (1978). Opportunities for detecting ultralong gravitational waves. Soviet Astronomy, 22, 36-38.

Detweiler, S. (1979). Pulsar timing and its application for detection of gravitational waves. Astrophysical Journal, 234, 1100-1104.

Arzoumanian, Z., et al. (2023). The NANOGrav 12.5 Year Data Release. Astrophysical Journal Letters, 951(1), L8.


Pupillometry

Pupil Size Measurement
Pupillary Response Analysis

Pupillometry is the measurement of changes in pupil size in response to cognitive, emotional, or perceptual stimuli. The pupil automatically dilates (mydriasis) during mental effort, emotional arousal, or approach-related states, and constricts (miosis) during relaxation or withdrawal. First documented systematically by Hess in the 1960s, pupillometry provides an objective, continuous measure of cognitive load, attention, and emotional response that complements behavioral and self-report measures.

Hess, E. H., & Polt, J. M. (1964). Pupil size in relation to mental activity during simple problem-solving. Science, 143(3611), 1190-1192.

Laeng, B., Sirois, S., & Gredebäck, G. (2012). Pupillometry: A window to the preconscious? Perspectives on Psychological Science, 7(1), 18-27.

Beatty, J. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91(2), 276-292.

eye-tracking-analysis cognitive-load-assessment arousal-measurement autonomic-nervous-system


Purposive sampling

judgmental sampling
selective sampling
criterion-based sampling
purposeful sampling

Michael Quinn Patton (systematic articulation); roots in early qualitative inquiry

Purposive sampling is a non-probability strategy in which the researcher deliberately selects participants, documents, or cases that are information-rich with respect to the research question. Rather than drawing units at random, the researcher applies explicit criteria aligned with the study's purpose, maximising the depth and relevance of the data collected. It is the default sampling logic in most qualitative research designs and is also used in mixed-methods and applied evaluative work.

Qualitative or quantitative data; participants, documents, or cases selected by researcher judgment

Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage.

Creswell, J. W. (2007). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (2nd ed.). Sage.

theoretical-sampling maximum-variation-sampling snowball-sampling convenience-sampling deviant-case-sampling typical-case-sampling


Push-Relabel Algorithm

preflow-push algorithm
Goldberg-Tarjan algorithm

The Push-Relabel Algorithm, developed by Andrew V. Goldberg and Robert E. Tarjan in 1988, is a highly efficient method for computing maximum flow in networks. Unlike augmenting path methods, it maintains a preflow and uses local push and global relabeling operations to drive flow toward the sink, achieving superior worst-case complexity.

Goldberg, A. V., & Tarjan, R. E. (1988). A new approach to the maximum flow problem. Journal of the ACM, 35(4), 921-940.

Goldberg, A. V. (1998). Recent advances in maximum flow and minimum-cost flow algorithms. In Algorithm Theory (pp. 1-10). Springer, Berlin.


Pushover Analysis

Pushover Analysis for Seismic Structural Assessment

Static pushover
Nonlinear static analysis

Pushover analysis is a nonlinear static method for assessing seismic structural performance. Introduced by Fajfar in 1996 as part of the N2 method, it progressively increases lateral loads on a structure until it reaches a target displacement, revealing how structures deform and yield under seismic events.

Nonlinear static method for earthquake engineering

Ahi, N., Desroches, R., & Jain, A. (1996). Lateral load distribution for equivalent static analysis of buildings. Engineering Journal, 33(2), 45-54.

Fajfar, P. (2000). A nonlinear analysis method for performance-based seismic design. Earthquake Spectra, 16(3), 573-592.

ASCE/SEI (2006). Seismic Rehabilitation of Existing Buildings (ASCE/SEI 41-06). American Society of Civil Engineers.

response-spectrum-analysis nonlinear-time-history-analysis incremental-dynamic-analysis equivalent-static-analysis


Pyraformer

Pyraformer (Pyramidal Attention for Long-Range Forecasting)

Pyramidal Attention Transformer
Pyraformer Transformer
Piramit Dikkat Dönüştürücüsü
Low-Complexity Transformer

Pyraformer is a Transformer-based model for long-range time-series forecasting introduced by Liu et al. at ICLR 2022. Its central innovation is a Pyramidal Attention Module (PAM) that organizes tokens into a multi-resolution hierarchy, enabling the model to capture temporal dependencies across multiple scales while keeping time and memory complexity at O(L log L) rather than the quadratic cost of vanilla self-attention.

Pyramidal self-attention transformer for time-series forecasting

Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., & Dustdar, S. (2022). Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. ICLR.


Q-Learning

Q-Learning (Off-Policy Temporal-Difference Control)

Q-learning algorithm
tabular Q-learning
off-policy TD control
Q-öğrenme

Q-learning, introduced by Christopher Watkins and Peter Dayan in 1992, is a model-free reinforcement-learning algorithm that learns the value of taking each action in each state — the Q-function — purely from experience, without a model of the environment. It is off-policy: it learns the optimal action-values while following an exploratory behaviour policy, and under standard conditions it provably converges to the optimal policy.

Model-free reinforcement-learning control algorithm

Off-policy (learns optimal Q while exploring)

Converges to optimal Q under standard conditions

Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

policy-gradient deep-reinforcement-learning dynamic-programming markov-switching-model


Q-Methodology

Q-Methodology is a mixed-methods approach that combines quantitative factor analysis with qualitative interpretation to identify distinct perspectives, viewpoints, or 'factors' shared by groups of people. Introduced by William Stephenson in 1935, it uses Q-sorts—where participants rank statements on a continuum—to measure subjective viewpoints systematically. The method applies factor analysis to correlations among Q-sorts (not items), revealing common patterns of opinion or attitude that transcend individual differences.

Stephenson, W. (1935). Technique of factor analysis. Nature, 136(3434), 297.

Brown, S. R. (1980). Political subjectivity: Applications of Q methodology in political science. Yale University Press.

McKeown, B., & Thomas, D. (2013). Q methodology (2nd ed.). Sage Publications.

repertory-grid factor-analysis principal-component-analysis thematic-analysis


Q-System

Q-System (NGI Index) for Rock Mass Classification

Norwegian Geotechnical Institute Classification

Nick Barton (Norwegian Geotechnical Institute)

The Q-System (NGI Index), introduced by Nick Barton and colleagues at the Norwegian Geotechnical Institute in 1974, is an alternative rock mass classification to RMR. It combines six parameters into a dimensionless index Q ranging from 0.001 to 1000, where higher Q values indicate better rock quality. The Q-System is particularly valued for tunnel and underground excavation design due to its explicit consideration of joint roughness and groundwater effects.

Empirical index for tunnel support and stability prediction

Barton, N., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics, 6(4), 189-236.

Barton, N. (2002). Some new Q-value correlations to assist in site characterisation and tunnel design. International Journal of Rock Mechanics and Mining Sciences, 39(2), 185-216.


QARDL

QARDL (Quantile Autoregressive Distributed Lag) combines quantile regression with ARDL modeling to estimate conditional relationships at different points of the distribution, revealing heterogeneous short-run and long-run effects. Introduced by Koenker and Xiao (2006) and refined by Cho et al. (2015), it captures how the effect of explanatory variables on outcomes varies across quantiles, essential for understanding tail behavior and distributional impacts rather than just mean effects.

Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.

Cho, J. S., Kim, H., & Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188(1), 281-300.


QLoRA

Deep Learning, Language Models, Parameter Efficient Fine-Tuning

QLoRA is an efficient fine-tuning method introduced by Dettmers et al. in 2023 that enables fine-tuning large language models using quantization and low-rank adaptation. By combining 4-bit quantization with LoRA, QLoRA reduces memory requirements by 75%, enabling fine-tuning of 65B-parameter models on single GPUs.

Dettmers, T., Pagnoni, A., Holtzman, A., & Contrastive, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv preprint arXiv:2305.14314.

direct-preference-optimization latent-diffusion-models masked-autoencoders mamba


QOLCE

epilepsy-specific pediatric quality of life

The QOLCE is a comprehensive 76-item disease-specific instrument developed by Sabaz et al. in 2000 to assess quality of life in children with epilepsy aged 4–16 years. Measuring across 16 distinct domains including seizure worry, cognitive concerns, medication effects, school/peer functioning, and family impact, the QOLCE provides a nuanced profile of how epilepsy and its treatment affect daily life. It exists in parent-report (QOLCE-P) and child self-report (QOLCE-C) versions.

Parent report; child self-report version available

Sabaz, M., Cairns, D. R., Lah, S., Williams, B., Gurrin, L., Connelly, A., & Berkovic, S. F. (2000). Validation of the Quality of Life in Childhood Epilepsy Questionnaire in Australian children with newly diagnosed and chronic epilepsy. Neurology, 55(9), 1646-1652.

Sabaz, M., Donnan, G., Anderson, V., & Berkovic, S. F. (2001). The health-related quality of life of children with newly diagnosed seizures. Epilepsia, 42(12), 1561-1567.

paqlq pedsql-diabetes child-health-questionnaire pedsql-cancer


The QOLIE-89 is a comprehensive disease-specific quality-of-life instrument developed specifically for people with epilepsy. Introduced by Devinsky and colleagues in 1995, it captures the broad impact of epilepsy on physical, emotional, social, and cognitive functioning. With 89 items organized into 17 distinct domains, it remains one of the most detailed QoL assessments for epilepsy and is widely used in clinical trials, health services research, and outcome monitoring.

Devinsky, O., Vickrey, B. G., Cramer, J., Edwards, B., Perrine, K., Hamberger, M. J., & Towle, V. L. (1995). Development of the Quality of Life in Epilepsy Inventory. Epilepsia, 36(11), 1089-1104.


QR-ARAS

QR-ARAS (qR-ARAS — q-Rung Orthopair extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)

Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


QR-COCOSO

QR-COCOSO (qR-CoCoSo — q-Rung Orthopair extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Kuvvetli, B. İ. (2023, JESD 11(4):1294-1309) — first published q-ROF CoCoSo application Peng, X. & Huang, H. (2020, TEDE 26(4):695-724) — algorithm source for q-ROF score function with hesitancy penalty Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019, Mgmt Decision 57(9):2501-2519) — crisp CoCoSo skeleton Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set in 2023. It turns a decision matrix of alternatives scored on


QR-CODAS

Naz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF distance measures (Euclidean + Hamming) Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set

QR-CODAS (qR-CODAS — q-Rung Orthopair extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Naz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF


QR-COPRAS

QR-COPRAS (qR-COPRAS — q-Rung Orthopair extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-EDAS

QR-EDAS (qR-EDAS — q-Rung Orthopair extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-GRA

QR-GRA (qR-GRA — q-Rung Orthopair extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-MABAC

QR-MABAC (qR-MABAC — q-Rung Orthopair extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-MARCOS

QR-MARCOS (qR-MARCOS — q-Rung Orthopair extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-MOORA

QR-MOORA (qR-MOORA — q-Rung Orthopair extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-PROMETHEE

QR-PROMETHEE (qR-PROMETHEE — q-Rung Orthopair extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-SAW

QR-SAW (qR-SAW — q-Rung Orthopair extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-TODIM

QR-TODIM (qR-TODIM — q-Rung Orthopair extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-TOPSIS

QR-TOPSIS (qR-TOPSIS — q-Rung Orthopair extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-VIKOR

QR-VIKOR (qR-VIKOR — q-Rung Orthopair extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-WASPAS

QR-WASPAS (qR-WASPAS — q-Rung Orthopair extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QR-WPM

QR-WPM (qR-WPM — q-Rung Orthopair extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


QSAR

Quantitative Structure-Activity Relationship Modeling

Quantitative Structure-Activity Relationship (QSAR) modeling predicts biological activity from molecular structure using statistical or machine learning models. Pioneered by Hansch in 1964, QSAR correlates numerical molecular descriptors with measured bioactivity, enabling prediction of activity for untested compounds and rational lead optimization.

Regression-based predictive modeling pipeline

Hansch, C. & Fujita, T. (1964). Rho-sigma-pi analysis. A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 1616-1626.

Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science, 22(1), 69-77.

Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623.


qSOFA Score

Quick Sequential Organ Failure Assessment Score

The Quick Sequential Organ Failure Assessment (qSOFA) score, introduced by the Sepsis-3 taskforce in 2016, is a rapid 3-variable bedside screening tool for identifying non-ICU patients at high risk of sepsis-related mortality. It uses altered mentation, systolic hypotension, and tachypnea to quickly stratify patients without requiring laboratory testing.

Singer, M., Deutschman, C. S., Seymour, C. W., et al. (2016). The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315(8), 801-810.

Seymour, C. W., Liu, V. X., Iwashyna, T. J., et al. (2017). Assessment of clinical criteria for sepsis: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315(8), 762-774.


QTL Mapping

Quantitative Trait Loci Mapping for Complex Trait Dissection

QTL analysis
Linkage mapping
Trait locus mapping

Quantitative trait loci (QTL) mapping is a genetic method that localizes chromosomal regions influencing quantitative traits—continuous phenotypes controlled by multiple genes and environmental factors. Developed by Lander and Botstein in 1989, QTL mapping uses linkage analysis and trait variation in segregating populations (such as F2 crosses or recombinant inbred lines) to identify genomic intervals containing loci that substantially affect trait values. This foundational approach has been extended to genome-wide association and is essential for understanding the genetic architecture of complex traits.

Lander, E. S., & Botstein, D. (1989). Mapping Mendelian traits using RFLP linkage maps. Genetics, 121(1), 185–199.

Haley, C. S., & Knott, S. A. (1992). A simple regression method for mapping quantitative trait loci using molecular markers. Heredity, 69(4), 315–324.

Kao, C. H., Zeng, Z. B., & Teasdale, R. D. (1999). Multiple interval mapping for quantitative trait loci. Genetics, 152(3), 1203–1216.

ibd-mapping ld-block-analysis polygenic-risk-score transmission-disequilibrium-test


Quadratic Discriminant Analysis

QDA
quadratic classifier
kuadratik diskriminant analizi

Classical Gaussian discriminant analysis (Fisher / Welch lineage)

Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.

Class-conditional Gaussians with separate covariances

Output: Class posterior probabilities

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.

linear-discriminant-analysis naive-bayes gaussian-mixture-model k-nearest-neighbors


Quadratic Programming

QP Optimization
Quadratic Optimization
Convex Quadratic Programming
İkinci Dereceden Programlama

Quadratic Programming (QP) is a class of constrained mathematical optimization in which the objective function is quadratic and the constraints are linear. Formalized by Frank and Wolfe (1956) through their gradient-based feasible-direction algorithm, QP is foundational in operations research, finance, machine learning, and engineering design wherever one must minimize a convex (or non-convex) quadratic cost subject to linear feasibility conditions.

Frank, M., & Wolfe, P. (1956). An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3(1–2), 95–110.


QUALIFLEX

QUALIFLEX (QUALItative FLEXible multiple criteria method) is a ranking multi-criteria decision-making (MCDM) method introduced by Paelinck, J. H. P. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Permutation concordance (all ranking permutations tested)

Paelinck, J. H. P. (1978). Qualiflex: A flexible multiple-criteria method. Economics Letters


Qualitative-Dominant Case-Focused Mixed Methods Design

case-embedded qualitative-priority mixed design

Creswell & Plano Clark; Teddlie & Tashakkori (priority notation)

Qualitative-dominant case-focused mixed methods design embeds quantitative evidence inside a primarily qualitative case study framework. The case — a bounded unit such as a school, organization, or community — is examined in depth through qualitative means, while quantitative data serve a secondary, supplementary role. Priority is firmly with the qualitative strand, which drives interpretation and findings.

Primarily qualitative (interviews, observation, documents) with supplementary quantitative data

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.


Qualitative-dominant concurrent embedded mixed methods

Qualitative-Dominant Concurrent Embedded Mixed Methods Design

QUAL-dominant embedded concurrent design

qualitative-priority embedded mixed methods

concurrent nested mixed methods (QUAL dominant)

Creswell & Plano Clark (embedded design); dominance weighting formalized in Teddlie & Tashakkori

A qualitative-dominant concurrent embedded mixed methods design collects qualitative and quantitative data simultaneously, but the qualitative strand carries the primary weight — it drives the research questions, generates the main findings, and frames interpretation. The quantitative strand is embedded within the larger qualitative study to provide supplemental support, context-setting, or triangulation, without displacing the qualitative logic at the core.

Primary qualitative data (interviews, observation, documents) with supplementary quantitative data collected concurrently

Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.


Qualitative-dominant explanatory sequential mixed methods

Qualitative-Dominant Explanatory Sequential Mixed Methods Design

QUAL-dominant explanatory sequential design

qual-priority explanatory sequential MMR

qualitative-weighted explanatory sequential design

2000s (formalized in Creswell & Plano Clark 2007, 2011, 2018)

Creswell & Plano Clark (explanatory sequential base); Morse (priority notation)

The qualitative-dominant explanatory sequential mixed methods design follows a two-phase sequential structure — quantitative data collected first, qualitative data collected second — while assigning dominant analytical weight to the qualitative strand. The quantitative phase surfaces statistical patterns that require deeper explanation; the qualitative phase, which carries greater interpretive authority in this variant, provides the rich contextual understanding that the numbers alone cannot supply.

Quantitative data (Phase 1) and qualitative data (Phase 2, dominant)

Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 189-208). Sage.


Qualitative-dominant exploratory sequential mixed methods

Qualitative-Dominant Exploratory Sequential Mixed Methods Design

QUAL-dominant exploratory sequential design

qualitative-priority exploratory sequential MMR

Creswell & Plano Clark; Morse (priority notation)

This design begins with a substantive qualitative phase (QUAL) that drives the study, followed by a smaller quantitative phase (quan) used to test, refine, or extend qualitative findings to a broader sample. The qualitative strand holds priority in both scope and interpretation; the quantitative strand serves a confirmatory or generalisability function. It is particularly well suited when theory or instrument development must be grounded in participants' own frameworks before statistical testing.

Qualitative data (phase 1, primary); quantitative data (phase 2, secondary/confirmatory)


Qualitative-Dominant Intervention Mixed Methods Design

qualitatively driven intervention design

qualitative-priority intervention mixed methods

Creswell & Plano Clark; Teddlie & Tashakkori

Qualitative-dominant intervention mixed methods is a research design in which qualitative inquiry carries primary theoretical and interpretive weight while quantitative data provide supplementary evidence, both strands applied within an intervention or program context. The design is used when understanding the lived experience of participants, the mechanisms of an intervention, and the meaning-making around change are more central to the research purpose than measuring effect sizes alone.

Qualitative data (primary) + quantitative data (supplementary); intervention/program context

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. SAGE Publications.


Qualitative-dominant mixed methods matrix

Qualitative-Dominant Mixed Methods Matrix Design

qualitative-priority mixed methods matrix

Charles Teddlie & Abbas Tashakkori (matrix framework); qualitative-dominant weighting drawn from Jennifer Greene and David Morgan

The qualitative-dominant mixed methods matrix is a design variant in which the researcher selects and positions a specific mixed methods design within a typological matrix — organized by timing (sequential vs. concurrent) and paradigm weighting — while assigning greater priority to the qualitative strand. Quantitative data play a supporting, supplementary role, and the final inferences are grounded primarily in qualitative findings.

Primarily qualitative (interviews, observations, documents) plus supplementary quantitative data


Qualitative-Dominant Mixed Methods Meta-Inference

qualitative-weighted mixed methods integration

Qualitative-dominant mixed methods meta-inference is the overarching inference-drawing process in a mixed methods study where qualitative findings carry primary explanatory weight. Meta-inference — the integrated conclusion drawn by combining qualitative and quantitative strands — is anchored to and interpreted through the richer, theoretically foregrounded qualitative findings, with quantitative results serving a supplementary, corroborating, or contextualizing function.

Qualitative primary strand + quantitative supplementary strand

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.

Tashakkori, A., & Teddlie, C. (2008). Quality of inferences in mixed methods research: Calling for an integrative framework. In M. M. Bergman (Ed.), Advances in Mixed Methods Research (pp. 101-119). SAGE Publications.


Qualitative-Dominant Multilevel Mixed Methods Design

qualitative-priority multilevel mixed methods

Tashakkori & Teddlie; Leech & Onwuegbuzie

Qualitative-dominant multilevel mixed methods design addresses research questions nested across two or more social levels — such as individuals within classrooms within schools — while assigning primary inferential weight to the qualitative strand. Quantitative data collected at one or more levels serve a supporting role: they contextualize, corroborate, or sharpen qualitative findings rather than generate the principal conclusions. The design is especially productive when understanding processes and meanings at multiple organizational layers is more important than population-level statistical estimates.

Qualitative (interviews, observations, documents) primary; quantitative (nested surveys, scales) secondary

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.


Qualitative-dominant multiphase mixed methods

Qualitative-Dominant Multiphase Mixed Methods Design

QUAL-dominant multiphase MMR
qualitative-priority multiphase design
qual-dominant multiphasic mixed design

Creswell & Plano Clark (multiphase base); Morse and Tashakkori & Teddlie (priority notation)

The qualitative-dominant multiphase mixed methods design combines multiple, sequentially or iteratively organized phases across a research program, with qualitative inquiry holding explicit priority. Quantitative data are collected in one or more supporting phases to supplement, refine, or validate the dominant qualitative strands. The design is common in longitudinal program evaluations, theory-building projects, and community-based participatory research where deep contextual understanding is the primary aim.

Qualitative data (primary) and quantitative data (supporting), collected across multiple sequential or iterative phases

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.


Qualitative-Dominant Pragmatic Mixed Methods Design

qualitative-priority pragmatic mixed methods

David L. Morgan; John W. Creswell & Vicki L. Plano Clark

Qualitative-dominant pragmatic mixed methods is a research design in which a qualitative strand carries the primary weight of the inquiry, while a smaller quantitative component adds breadth or corroboration. Grounded in pragmatism as its philosophical framework, the design treats questions of data type, sequence, and integration as practical choices driven by the research problem rather than by methodological ideology.

Primary qualitative data (interviews, observation, documents) supplemented by quantitative data (surveys, counts, scales)

Morgan, D. L. (2014). Integrating Qualitative and Quantitative Methods: A Pragmatic Approach. Sage.


Qualitative-Dominant Transformative Mixed Methods Design

transformative mixed methods with qualitative priority

Donna M. Mertens (transformative framework); John W. Creswell & Vicki L. Plano Clark (weighting typology)

Qualitative-dominant transformative mixed methods is a mixed methods research design in which qualitative data carry the primary evidential weight while quantitative data serve a supplementary role, and the entire inquiry is governed by a transformative theoretical framework — one committed to social justice, equity, and the amplification of marginalized voices. The design produces rich contextual understanding alongside statistical corroboration to advance emancipatory or advocacy-oriented goals.

Primarily qualitative (interviews, focus groups, observation, documents) supplemented by quantitative data

Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.


Qualitative Meta-Synthesis

Qualitative Meta-Synthesis (Thematic Meta-Synthesis)

Qualitative Evidence Synthesis
Thematic Synthesis
Metasynthesis
Qualitative Systematic Review

Sandelowski & Barroso (2007), Popularized by Thomas & Harden (2008)

Qualitative meta-synthesis is a systematic method for synthesizing findings from multiple qualitative research studies (interviews, focus groups, ethnographies) to develop integrated interpretations and theoretical insights. Formalized by Sandelowski and Barroso (2007) and popularized by Thomas and Harden (2008), qualitative meta-synthesis preserves the rich, contextual, interpretive nature of qualitative evidence while enabling broader conclusions across multiple studies. Unlike quantitative meta-analysis, which pools numbers, qualitative meta-synthesis synthesizes themes, meanings, and conceptual insights—answering questions like 'How do cancer patients experience treatment side effects?' or 'What factors shape patient engagement with preventive health programs?' across multiple studies.

Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, 45.

Sandelowski, M., & Barroso, J. (2007). Handbook for Synthesizing Qualitative Research. Springer Publishing Company.

Britten, N., Campbell, R., Pope, C., Donovan, J., Morgan, M., & Pill, R. (2002). Using meta ethnography to synthesise qualitative research: a worked example. Journal of Health Services Research & Policy, 7(4), 209–215.

systematic-review thematic-analysis qualitative-research-methods realist-synthesis evidence-synthesis-framework


Qualitative-Priority Mixed Methods Research Design

QUAL-dominant mixed methods
qualitative-dominant mixed design
qual-priority MMR
qualitative-weighted mixed methods

1991–2003 (formalized in mixed methods typologies)

Janice Morse; John W. Creswell & Vicki L. Plano Clark

Qualitative-priority mixed methods design is a mixed methods approach in which qualitative inquiry carries the greater weight — in terms of volume, analytical depth, and interpretive authority — while a supplementary quantitative strand provides supporting evidence. The design acknowledges that the phenomenon under study is best understood through meaning-making, lived experience, or social processes, with numbers used to corroborate or contextualize, not to dominate, the research story.

Primarily qualitative (text, interviews, observations) supplemented by quantitative (numeric) data

Morse, J. M., & Niehaus, L. (2009). Mixed Method Design: Principles and Procedures. Left Coast Press.


Qualitative Research Overview

Qualitative Research: Approaches, Methods, and Analysis

qualitative inquiry
exploratory research
interpretive research

Ethnographers (Boas), grounded theorists (Glaser & Strauss, 1967), phenomenologists, and interpretivists (1900s–1980s)

Qualitative research is a systematic inquiry into human experiences, meanings, behaviors, and contexts using non-numerical data (words, text, images, observations). Unlike quantitative research, which seeks to measure variables and test hypotheses numerically, qualitative research prioritizes depth, contextual richness, and understanding of 'how' and 'why.' Major approaches include phenomenology (lived experiences), grounded theory (theory development from data), ethnography (cultural understanding through immersion), case study (in-depth investigation of a specific case), and narrative inquiry (personal stories). Creswell (2017), Braun and Clarke (2006), and Patton (2015) provide contemporary frameworks for qualitative design, data collection, and analysis.

Creswell, J. W. (2017). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.


Trustworthiness Criteria in Qualitative Research

Lincoln and Guba's Framework for Assessing Qualitative Research Rigor

trustworthiness criteria
credibility
dependability
confirmability
transferability
qualitative quality

Trustworthiness is a framework for evaluating the quality and rigor of qualitative research, developed by Lincoln and Guba (1985) as an alternative to quantitative criteria (internal validity, external validity, reliability, objectivity). The framework comprises five criteria: credibility (findings are accurate and grounded in data), transferability (findings apply to other contexts), dependability (findings are consistent and defensible), confirmability (findings reflect the data and participants' perspectives, not researcher bias), and authenticity (research reflects diverse viewpoints and promotes understanding). This framework has become standard for assessing qualitative research across disciplines and guides researchers in designing and reporting rigorous qualitative studies.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. SAGE Publications.

Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research (pp. 105-117). SAGE Publications.

Tracy, S. J. (2010). Qualitative quality: Eight big-tent criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851.

Whittemore, R., Chase, S. K., & Mandle, C. L. (2001). Validity in qualitative research. Qualitative Health Research, 11(4), 522-537.

member-checking reflexivity-in-research participant-observation in-depth-interview-method


Qualitative Evidence Synthesis Methods

Systematic Review and Meta-Synthesis of Qualitative Research

systematic review of qualitative studies

Qualitative evidence synthesis (QES) is a systematic method for combining and interpreting findings from multiple qualitative research studies to generate higher-level understanding and theory. Different approaches—meta-ethnography, thematic synthesis, meta-narrative review, critical interpretive synthesis—each have distinct philosophical underpinnings and analytical procedures. Introduced by Noblit and Hare (1988) with meta-ethnography, qualitative synthesis has evolved alongside systematic reviews of quantitative research. Unlike quantitative meta-analysis, which pools numerical effect sizes, qualitative synthesis integrates concepts, themes, and interpretations from primary studies, identifying patterns, conflicts, and emergent theory. QES is increasingly used in health research, social sciences, and education to understand complex phenomena, translate research into practice, and identify gaps in evidence.

Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing Qualitative Studies. SAGE Publications.

Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8(1), 45.

Sandelowski, M., Docherty, S., & Emden, C. (2006). Qualitative metasynthesis: Issues and techniques. Research in Nursing & Health, 20(4), 365-371.

Britten, N., Campbell, R., Pope, C., Donovan, J., Morgan, M., & Pill, R. (2002). Using meta ethnography to synthesise qualitative research: A worked example. Journal of Health Services Research & Policy, 7(4), 209-215.

document-analysis thematic-analysis qualitative-rigor-criteria nvivo-atlas-qualitative


Quality-Adjusted Life Year

Alan Williams (Health Economics Research Centre, Oxford University)

A QALY measures health benefit as utility weight (0 = death, 1 = perfect health) multiplied by time lived. Developed by Alan Williams in 1985, QALYs enable comparison of disparate health interventions on a common metric. Used globally by health technology assessment bodies—NICE (UK), HAS (France), CADTH (Canada), WHO—to decide which treatments deserve public funding.

Kind, P. (1989). The EuroQol instrument: an index of health-related quality of life. In B. Teeling Smith (Ed.), Measuring health: a practical approach. Chichester: Wiley.

Weinstein, M. C., & Stason, W. B. (1976). Hypertension: A Policy Perspective. Cambridge, MA: Harvard University Press.

Brooks, R. (1996). EuroQol: the current state of play. Health Policy, 37(1), 53-72.

disability-adjusted-life-year cost-effectiveness-analysis markov-model-health-economics decision-analytic-modeling willingness-to-pay


Quality Function Deployment

Quality Function Deployment (House of Quality)

QFD
House of Quality
customer-driven engineering
voice of the customer matrix

1966 (Japan); popularised in the West ~1988

Quality Function Deployment (QFD) is a structured method for translating customer needs — the voice of the customer — into specific technical requirements at every stage of product or service development. Originating in Japan in the 1960s, QFD uses a matrix-based tool called the House of Quality to make customer priorities visible, link them to engineering parameters, expose trade-offs, and maintain focus on what customers actually value throughout the design process.

Structured quality planning and product design method

Customer requirements (qualitative), engineering metrics (quantitative), competitive benchmarking data

Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.

Hauser, J. R., & Clausing, D. (1988). The house of quality. Harvard Business Review, 66(3), 63–73.

failure-mode-and-effects-analysis design-of-experiments taguchi-method statistical-process-control six-sigma-dmaic benchmarking


Quandt-Andrews Test

Quandt-Andrews (sup-Wald) Unknown Breakpoint Test

sup-Wald Test
Andrews Breakpoint Test
Unknown Structural Break Test
Quandt Likelihood Ratio Test

The Quandt-Andrews test, formalized by Andrews (1993), detects structural breaks in regression parameters when the breakpoint date is unknown a priori. It sweeps all candidate break dates within a trimmed interior of the sample, computes a Wald (or LM/LR) statistic at each candidate, and reports the supremum of those statistics. Applied economists and time-series analysts use it to test whether coefficients remain stable across a full estimation window without needing to specify when the break occurred.

Parameter stability across the full sample

Non-standard; tabulated by Andrews (1993)

Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856.


Quantile-on-Quantile Regression

QQ regression
QQ approach
quantile-on-quantile approach
nonparametric quantile regression

Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.

Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.

Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.

quantile-regression nonlinear-ardl arma-model vector-autoregression granger-causality-test dcc-garch-model


Nonparametric Quantile Regression

Quantile Regression (Nonparametric Variants)

Kantil Regresyon (Nonparametric Varyantlar)

Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.

Quantile regression (nonparametric variants)

Minimisation of the asymmetric (pinball/check) loss

Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.

Koenker, R. (2005). Quantile Regression. Cambridge University Press.

ols-regression theil-sen-estimator kernel-density-test ridge-regression lasso-regression


Quantile Regression

conditional quantile regression
regression quantiles
Kantil Regresyon

Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.

Minimisation of asymmetrically weighted absolute residuals (check loss)

Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.

ols-regression ridge-regression lasso-regression poisson-regression panel-fixed-effects


Quantile VAR

Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times.

Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.

White, H., Kim, T. H., & Manganelli, S. (2015). VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics, 187(1), 169-188.


Quantitative Content Analysis

QCA
manifest content analysis
systematic content analysis
frequency-based content analysis

1950s (Berelson 1952; Krippendorff 1980/2004)

Bernard Berelson; later systematised by Klaus Krippendorff

Quantitative content analysis is a systematic, replicable method for converting the manifest content of text, images, or other recorded communication into numerical data. By applying a pre-specified codebook to a defined corpus and counting or scaling the resulting categories, researchers obtain frequency distributions, proportions, and relationships that can be subjected to standard statistical tests. It is the dominant method for large-scale, objective analysis of media, documents, social media posts, policy texts, and similar materials.

Quantitative observational research method

Text, images, audio, video — any recorded communication coded into numeric categories

Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage.

Berelson, B. (1952). Content Analysis in Communication Research. Free Press.


Quantitative Descriptive Analysis

Quantitative Descriptive Analysis (QDA) is a comprehensive sensory evaluation method developed by Stone and colleagues in the 1970s that uses a trained panel to describe the intensity of sensory attributes in food products. QDA provides detailed, quantitative profiles of flavor, aroma, texture, and appearance, allowing researchers and product developers to characterize and compare products objectively.

Stone, H., Bleibaum, R. N., & Thomas, H. A. (2012). Sensory evaluation practices (4th ed.). Academic Press.

Lawless, H. T., & Heymann, H. (2010). Sensory evaluation of food: Principles and practices (2nd ed.). Springer.


Quantitative-Dominant Case-Focused Mixed Methods Design

quantitatively weighted case mixed methods

dominant-status case-centered mixed design

Tashakkori & Teddlie (dominant-status typology); Creswell & Plano Clark (design taxonomy)

Quantitative-dominant case-focused mixed methods organizes a study around one or more clearly bounded cases while assigning primary weight and inferential authority to quantitative data. Qualitative data are collected within the same case boundaries and serve an augmenting, explanatory, or contextual role rather than an equal one. The design is ideal when a case (a school, organization, community, or patient cohort) is the unit of analysis and the core research questions require measurable outcomes that qualitative evidence then helps interpret or explain.

Quantitative (primary: surveys, tests, structured instruments) + qualitative (secondary: interviews, documents, observations) within one or more bounded cases

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.


Quantitative-dominant concurrent embedded mixed methods

Quantitative-Dominant Concurrent Embedded Mixed Methods Design

concurrent embedded design (QUAN priority)

quantitative-primary embedded mixed methods

A mixed methods design in which a dominant quantitative study (survey, experiment, or other large-scale numeric inquiry) is conducted simultaneously with a smaller, embedded qualitative component. The qualitative strand serves a secondary, supporting role — such as explaining mechanisms, capturing participant experience, or monitoring implementation — while the quantitative strand drives the primary research questions and conclusions. Both strands run concurrently rather than sequentially.

Primarily quantitative (surveys, experiments, tests) with embedded qualitative data (interviews, observations)

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.

Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 209–240). Sage Publications.


Quantitative-dominant concurrent triangulation mixed methods

Quantitative-Dominant Concurrent Triangulation Mixed Methods Design

concurrent triangulation with quantitative priority

The quantitative-dominant concurrent triangulation mixed methods design collects quantitative (QUAN) and qualitative (qual) data simultaneously, with quantitative data carrying the primary weight. The two strands are analyzed independently and then compared or merged to triangulate findings, with the smaller qualitative strand serving to corroborate, elaborate, or nuance the quantitative results. The explicit QUAN priority means that the research questions, sampling logic, and conclusions are primarily anchored in the quantitative component.

Quantitative (primary) and qualitative (secondary) data collected concurrently

Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.

Morgan, D. L. (1998). Practical strategies for combining qualitative and quantitative methods: Applications to health research. Qualitative Health Research, 8(3), 362–376.


Quantitative-dominant explanatory sequential mixed methods

Quantitative-Dominant Explanatory Sequential Mixed Methods Design

QUAN-dominant explanatory sequential design

quan-priority explanatory sequential MMR

weighted explanatory sequential mixed methods

2007 (first edition of Designing and Conducting Mixed Methods Research)

The quantitative-dominant explanatory sequential mixed methods design is a two-phase mixed methods approach in which a larger, primary quantitative study is conducted first, followed by a smaller, secondary qualitative phase that explains, elaborates, or contextualises the quantitative results. Quantitative evidence carries the greater weight in answering the research questions, while qualitative data provide interpretive depth for puzzling or unexpected statistical findings.

Quantitative data (primary) and qualitative data (secondary/follow-up)

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.


Quantitative-Dominant Intervention Mixed Methods Design

quantitatively weighted intervention mixed methods

quantitative-priority intervention mixed methods

2007–2011 (codified in Creswell & Plano Clark editions)

Creswell & Plano Clark (intervention MMD); weighting framework systematized in mixed methods typologies

Quantitative-dominant intervention mixed methods design embeds a qualitative component within a predominantly quantitative intervention study — typically a randomized controlled trial or quasi-experiment — where the quantitative strand carries the primary weight in determining efficacy, while the qualitative strand explains the processes, mechanisms, or participant experiences that illuminate why and how the intervention works.

Primarily quantitative (outcome/efficacy data); supplementary qualitative (process/experience data)

Brundisini, F., Giacomini, M., DeJean, D., Vanstone, M., Winsor, S., & Smith, A. (2013). Chronic disease patients' experiences with accessing health care in rural and remote areas: A systematic review and qualitative meta-synthesis. Ontario Health Technology Assessment Series, 13(15), 1–33.


Quantitative-Dominant Mixed Methods Meta-Inference

QUAN-dominant meta-inference
quantitatively weighted meta-inference
QUAN-priority integration inference
quantitative-weighted mixed inference

Tashakkori & Teddlie (meta-inference concept); Creswell & Plano Clark (dominance weighting framework)

Quantitative-dominant mixed methods meta-inference is an integration procedure in which the researcher draws an overarching conclusion by combining inferences from both quantitative and qualitative strands, while explicitly assigning greater evidential weight to the quantitative results. The qualitative strand serves a supporting, elaborating, or contextualizing role rather than an equal voice in the final interpretation.

Combined quantitative (primary) and qualitative (secondary) datasets

Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.


Quantitative-Dominant Multilevel Mixed Methods Design

multilevel mixed methods with quantitative priority

dominant-status multilevel mixed methods

Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie

Quantitative-dominant multilevel mixed methods design is a mixed methods approach in which quantitative inquiry carries the primary evidential weight while qualitative data play an auxiliary, illuminating role, and both strands are applied across two or more hierarchically nested levels of analysis — for example, students within classrooms within schools. The design is suited to research questions that require both statistical modeling of nested structures and contextual understanding of how those structures operate.

Nested quantitative data (surveys, tests, administrative records) supplemented by qualitative data (interviews, documents) at one or more levels

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications.


Quantitative-dominant multiphase mixed methods

Quantitative-Dominant Multiphase Mixed Methods Design

multiphase mixed methods with quantitative priority

Creswell & Plano Clark (multiphase framework); Tashakkori & Teddlie (priority notation)

A quantitative-dominant multiphase mixed methods design conducts a series of distinct research phases — at least two, often three or more — in which quantitative data and analyses bear the primary weight of answering the research questions, while qualitative components serve a supporting or explanatory role. Phases are linked by explicit integration points where findings from one phase inform the design or interpretation of the next.

Quantitative (primary) and qualitative (secondary) data across multiple phases

Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.


Quantitative-dominant pragmatic mixed methods

Quantitative-Dominant Pragmatic Mixed Methods Design

pragmatic quantitative-priority mixed design

Tashakkori & Teddlie (mixed methods paradigm discourse); pragmatic strand systematized by Morgan and Creswell

A mixed methods design in which quantitative data and analysis carry the primary explanatory weight while a smaller qualitative component provides contextual depth. Grounded in philosophical pragmatism, design decisions — including timing, sequencing, and the scope of each strand — are driven by what best answers the research question rather than by adherence to a single paradigmatic tradition.

Primarily quantitative (surveys, tests, administrative records); supplemented by qualitative (interviews, observations)

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE Publications.


Quantitative-Dominant Transformative Mixed Methods Design

transformative mixed methods (quantitative priority)

advocacy-framed mixed methods (quantitative emphasis)

2003–2007 (Mertens); systematised 2011 (Creswell & Plano Clark)

Donna M. Mertens (transformative paradigm); John W. Creswell & Vicki L. Plano Clark (mixed methods typology)

Quantitative-dominant transformative mixed methods design embeds a transformative theoretical lens — such as feminist, critical race, or disability theory — as the overarching framework of a study while assigning greater weight and priority to quantitative data collection and analysis. Qualitative data play a secondary, supplementary role, typically contextualising or deepening statistical findings. The design is common in social justice, equity, and advocacy-oriented applied research where large-scale measurement is essential but must be grounded in an explicit commitment to marginalized communities.

Quantitative data (primary) + qualitative data (secondary/supplementary)

Mertens, D. M. (2007). Transformative paradigm: Mixed methods and social justice. Journal of Mixed Methods Research, 1(3), 212–225.


Quantitative-Priority Mixed Methods Research Design

QUAN-dominant mixed methods
quantitative-dominant mixed methods
quan-priority design
quantitative-first mixed methods

Creswell & Plano Clark; Teddlie & Tashakkori

Quantitative-priority mixed methods design is a research approach in which quantitative data and analysis carry the primary explanatory weight, while qualitative data play a supplementary or corroborating role. The researcher collects and analyzes quantitative data first (or concurrently with greater emphasis), then uses qualitative findings to elaborate, explain, or contextualize the statistical results. Priority and sequence together define where integration occurs and how each strand informs the other.

Primarily quantitative (surveys, instruments, experiments); supplementary qualitative (interviews, documents)

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.


Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) is a post-processing technique that converts MRI phase data into quantitative susceptibility values, enabling direct visualization and measurement of tissue magnetic properties. Developed by Wang, Liu, and colleagues, QSM transforms phase shifts caused by differences in magnetic susceptibility between tissues into tissue-specific biomarkers. It has revolutionized the sensitivity of MRI to iron, calcium, and other paramagnetic and diamagnetic substances, making it valuable in neurodegenerative disease diagnosis and tissue characterization.

Wang, Y., Liu, T. (2015). Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magnetic Resonance in Medicine, 73(1), 82-101.

Liu, T., Spincemaille, P., de Rochefort, L., et al. (2009). Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from undersample phase data. Magnetic Resonance in Medicine, 66(3), 579-592.

Haacke, E. M., Xu, Y., Cheng, Y. C., Reichenbach, J. R. (2004). Susceptibility weighted imaging (SWI). Magnetic Resonance in Medicine, 52(3), 612-618.

dti-tractography ct-iterative-reconstruction pet-kinetic-modeling oct-angiography functional-ultrasound


QAOA
quantum alternating operator ansatz

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm designed to solve combinatorial optimization problems on near-term quantum devices. Introduced by Farhi, Goldstone, and Gutmann in 2014, QAOA encodes optimization problems into quantum circuits and uses classical optimization to tune circuit parameters, aiming to find approximately optimal solutions for problems like MaxCut, graph coloring, and scheduling.

Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.

Zhou, L., Wang, S. T., Choi, S., et al. (2020). Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Physical Review X, 10, 021067.

Hadfield, S., Wang, Z., O'Gorman, B., et al. (2019). From the Ising model to QAOA: A quantum optimization algorithm from the physicist's perspective. Algorithms, 12, 34.

variational-quantum-eigensolver quantum-phase-estimation quantum-monte-carlo grovers-algorithm


Quantum Key Distribution (BB84)

Quantum Key Distribution (QKD) BB84 is a cryptographic protocol allowing two parties to establish a shared secret key using quantum mechanics. Proposed by Bennett and Brassard in 1984, BB84 provides information-theoretic security: an eavesdropper's presence is guaranteed to be detected, and the secret key is provably secure against unlimited computational power.

Bennett, C. H., Brassard, G. (1984). Quantum cryptography: public key distribution and coin tossing. Proceedings of IEEE International Conference on Computers, Systems, and Signal Processing, 175–179.

Shor, P. W., Preskill, J. (2000). Simple proof of security of the BB84 quantum key distribution protocol. Physical Review Letters, 85, 441–444.

Renner, R. (2008). Security of quantum key distribution. arXiv preprint arXiv:0512258.


Quantum Monte Carlo

QMC
variational Monte Carlo
diffusion Monte Carlo

Quantum Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems.

Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.

Reynolds, P. J., Tobochnik, J., Gould, H. (1990). Diffusion quantum Monte Carlo. Computers in Physics, 4, 662–668.

Needs, R. J., et al. (2020). Variational and diffusion quantum Monte Carlo calculations with the CASINO code. The Journal of Chemical Physics, 152, 154106.


Quantum Phase Estimation

Quantum Phase Estimation (QPE) is a fundamental quantum subroutine that estimates the eigenvalues of a unitary operator. Developed by Alexei Kitaev in 1995, QPE combines controlled unitary evolution with the quantum Fourier transform to extract eigenvalues from quantum states with exponential precision scaling.

Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026.

Cleve, R., Ekert, A., Macchiavello, C., Mosca, M. (1998). Quantum algorithms revisited. Proceedings of the Royal Society A, 454, 339–354.

Aspuru-Guzik, A., Love, P. J., Love, P. J. (2005). Simulated quantum computation of molecular energies. Science, 309, 1704–1707.


Quantum SVM

Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd

Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm combining quantum feature spaces with classical SVM training. Proposed by Rebentrost et al. in 2014, QSVM leverages quantum processors to compute kernel functions, potentially offering speedup for classification problems while remaining practical on near-term quantum devices.

Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503.

Havlíček, V., Córcoles, A. D., Temme, K., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567, 209–212.

Liu, Y., Arunachalam, S., Temme, K. (2021). A rigorous and robust quantum speed-up in supervised machine learning. arXiv preprint arXiv:2010.07471.


Quantum Teleportation

Quantum Teleportation is a protocol for transferring an unknown quantum state between distant parties using entanglement and classical communication. Discovered by Bennett et al. in 1993, teleportation violates no fundamental principles but demonstrates the power of entanglement: an unknown quantum state can be reconstructed at a distant location without ever being transmitted.

Bennett, C. H., Brassard, G., Crépeau, C., Jozsa, R., Peres, A., Wootters, W. K. (1993). Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels. Physical Review Letters, 70, 1895–1899.

Bouwmeester, D., et al. (1997). Experimental quantum teleportation. Nature, 390, 575–579.

Ma, X. S., et al. (2012). Quantum teleportation over 143 kilometres using active feed-forward. Nature, 489, 269–273.


Quasi-Geostrophic Omega Equation

Quasi-Geostrophic Omega Equation for Vertical Motion

QG omega equation
Quasi-geostrophic dynamics
Vertical motion prediction

The quasi-geostrophic (QG) omega equation is a fundamental diagnostic equation in synoptic meteorology that relates vertical motion (omega = dP/dt) to horizontal temperature and vorticity fields. It predicts where air rises and sinks based on the geostrophic flow structure without explicitly solving for vertical velocity.

Holton, J. R. (2004). An Introduction to Dynamic Meteorology (4th ed.). Academic Press.

Navarra, A., & Simmons, A. J. (2006). Instability of a truncated Eady model. Journal of the Atmospheric Sciences, 51(7), 1033-1051.


Quaternion Attitude

Quaternion Attitude Representation and Kinematics

quaternion representation
attitude kinematics
q-vector

William Hamilton (quaternions), aerospace engineers

Quaternion attitude representation is a mathematical framework for describing three-dimensional rotations using four-dimensional vectors (quaternions). Superior to Euler angles due to the absence of singularities (gimbal lock), quaternions are the standard representation in modern attitude estimation, spacecraft control, and 3D computer graphics. Quaternion kinematics elegantly expresses how attitude evolves under angular velocity measurements from gyroscopes.

Shuster, M. D. (1993). A survey of attitude representations. Journal of the Astronautical Sciences, 41(4), 439–517.

Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.

Savage, P. G. (2000). Strapdown inertial navigation integration algorithm design. Part 1: Attitude algorithms. Journal of Guidance, Control, and Dynamics, 21(1), 19–28.


Query Optimization

Query optimization is a critical process in database management that transforms high-level SQL queries into efficient execution plans. Developed systematically by IBM researchers in the late 1970s, it aims to minimize execution time, disk I/O, and resource consumption by selecting the most effective access paths and join strategies.

Jarke, M., & Koch, J. (1984). Query optimization in database systems. ACM Computing Surveys, 16(2), 111-152.

Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie, R. A., & Price, T. G. (1979). Access path selection in a relational database management system. Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, 23-34.

Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.

database-normalization indexing-strategy execution-plan-analysis cost-estimation join-optimization


Quest Scale

The Quest Scale, developed by Batson and Ventis (1976), is a 12-item self-report measure of a third religious orientation beyond Allport and Ross's intrinsic and extrinsic religiosity. The 'quest' orientation reflects an open, questioning approach to religion: someone who views faith as an ongoing journey of exploration and doubt rather than a settled worldview or instrumental tool. High quest scorers embrace existential uncertainty, seek genuine answers to life's deepest questions, and are comfortable with religious doubt and revision. The scale has become important in understanding mature religious development and predicting prosocial behavior, openness, and psychological flexibility.

Batson, C. D., & Ventis, W. L. (1982). The Religious Experience: A Social-Psychological Perspective. Oxford University Press. ISBN: 9780195030761.

Batson, C. D., Schoenrade, P. A., & Ventis, W. L. (1993). Religion and the Individual: A Social-Psychological Perspective. Oxford University Press. ISBN: 9780195089073.

intrinsic-extrinsic-religiosity daily-spiritual-experience-scale duke-religion-index systems-belief-inventory


Question Answering

QA
machine reading comprehension
Soru Cevaplama (Question Answering)

Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.

Input: A natural-language question plus a context passage

Output: An answer in natural language

Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.

Yang, Z. et al. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. NeurIPS.

sentiment-analysis machine-translation named-entity-recognition text-classification


Queueing Simulation

Queueing Simulation — Stochastic Simulation of Waiting-Line Systems

Queue Simulation
Queuing Theory Simulation
Waiting-Line Simulation
DES-Queue

Queueing Simulation combines classical queueing theory with discrete-event simulation to model systems where entities arrive, wait for service, and depart. It predicts performance metrics such as average waiting time, queue length, and server utilization, enabling capacity planning and bottleneck identification across service, manufacturing, healthcare, and network systems.

Stochastic simulation / analytical modeling

Arrival times, service times, queue discipline parameters

Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York.

Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education, New York.

discrete-event-simulation monte-carlo-simulation markov-model system-dynamics agent-based-modeling stochastic-queueing-simulation


Queuing Theory in Healthcare

Queuing Theory for Healthcare Service Management and Wait Time Analysis

Healthcare Queuing
Queue Management Healthcare

Queuing theory is a mathematical discipline that models waiting lines, service capacity, and customer (patient) flow. Developed initially by Agner Erlang for telecommunications in 1909, it has been extensively applied to healthcare to analyze and optimize emergency departments, outpatient clinics, surgical suites, and diagnostic service centers.

Stochastic modeling and optimization technique

Erlang, A. K. (1909). The theory of probabilities and telephone conversations. Nyt Tidsskrift for Matematik, 20(B), 33–39.

Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their application to the theory of failures. Annals of Mathematical Statistics, 24(3), 338–354.

Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2008). Fundamentals of Queuing Theory (4th ed.). John Wiley & Sons.

patient-flow-simulation hospital-bed-occupancy-model lean-healthcare staffing-ratio-analysis dea-hospital-efficiency


Quick Inventory of Depressive Symptomatology-Self Report

The Quick Inventory of Depressive Symptomatology is a 16-item assessment designed by A. John Rush and colleagues to efficiently measure the severity of depressive symptoms in adults. Published in Biological Psychiatry in 2003, the QIDS exists in both self-report (QIDS-SR) and clinician-rated (QIDS-C) versions. It was developed as a brief alternative to the longer Inventory of Depressive Symptomatology (IDS, 30 items) while maintaining comprehensive coverage of DSM-IV depressive symptoms. The QIDS has become a standard outcome measure in treatment research, particularly in large comparative effectiveness trials.

Self-report or clinician-administered questionnaire

Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., & Ninan, P. T. (2003). The 16-item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry, 54(5), 573–583.

Trivedi, M. H., Rush, A. J., Ibrahim, H. M., Carmody, T. J., Biggs, M. M., Suppes, T., & Crismon, M. L. (2004). The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) for the evaluation of major depressive disorder. Depression and Anxiety, 20(3), 123–126.

Berndt, E. R., Koran, L. M., Roness, L. A., Trivedi, M. H., Herman, B. K., & Lee, J. C. (2000). Lost human productivity from US cancer mortality. Journal of Clinical Psychiatry, 61(9), 630–636.

phq-9 bdi-ii montgomery-asberg-depression patient-global-impression-change


Quota Sampling

quota-controlled sampling
quota selection
non-probability quota sampling

Developed in market research and opinion polling, notably applied by George Gallup in the 1930s

Quota sampling is a non-probability technique in which the researcher pre-specifies how many units to recruit from each subgroup (quota cell) defined by one or more control variables such as age, gender, or occupation. Interviewers or data collectors then use their own judgment to find and enroll participants until each cell is filled. The method guarantees the sample mirrors the population on the control variables but does not provide the randomness needed for classical statistical inference.

Categorical population characteristics; survey or interview data

Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann.

stratified-sampling purposive-sampling convenience-sampling proportional-stratified-sampling cluster-sampling systematic-sampling


R-squared

R²
coefficient of determination
r2 score

The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data.

Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318.

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.

Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society A, 222, 309-368.

adjusted-r-squared root-mean-squared-error mean-absolute-error akaike-information-criterion bayesian-information-criterion


Racism and Life Experiences Scales

The Racism and Life Experiences Scales (RaLES) are a multidimensional assessment designed to measure the frequency and intensity of racism-related stress experienced by people of color. Developed by Harrell in 2000, the RaLES operationalize racism not as a single phenomenon but as a constellation of stressors across multiple life domains—individual encounters, collective experiences, institutional discrimination, and historical trauma. The instrument is used in health research to evaluate the psychosocial burden of racism and to understand mechanisms linking discrimination to mental and physical health disparities.

Harrell, S. P. (2000). A multidimensional conceptualization of racism-related stress: Implications for the well-being of people of color. American Journal of Orthopsychiatry, 70(1), 42–57.

cultural-competence-assessment social-distance-scale ethnic-identity-scale cultural-humility-scale


Radial Velocity Method

Radial Velocity Exoplanet Detection Method

Doppler method
spectroscopic velocity measurement

The radial velocity method detects exoplanets by measuring the Doppler shift of a star's spectral lines caused by gravitational tugging from orbiting planets. When a planet orbits a star, the star wobbles slightly toward and away from Earth, creating periodic shifts in its light spectrum. First proposed by Friedrich Wilhelm Bessel in the 19th century and successfully applied to exoplanet detection in 1995, this method has discovered nearly half of all known exoplanets.

Mayor, M., & Queloz, D. (1995). A Jupiter-mass companion to a solar-type star. Nature, 378(6555), 355-359.

Campbell, B., Walker, G. A., & Yang, S. (1988). A Search for Substellar Companions to Solar-type Stars. The Astrophysical Journal, 331, 902.

Pepe, F., et al. (2014). Exoplanet research with the HARPS-N spectrograph at the Telescopio Nazionale Galileo. Astronomy & Astrophysics, 534, A58.


Radiation Dose Assessment

Radiation Dose Assessment and Evaluation

dose calculation
exposure assessment
radiation hazard evaluation

Radiation dose assessment is a systematic evaluation of human exposure to ionizing radiation from external or internal sources, formalized by the International Commission on Radiological Protection (ICRP) in the late 20th century. It combines radiation transport calculations with biological effect models to quantify absorbed dose, equivalent dose, and effective dose for worker safety and public health protection.

computational health assessment pipeline

International Commission on Radiological Protection (2007). The 2007 Recommendations of the ICRP. Publication 103. Annals of the ICRP, 37(2–4).

Shultis, J. K., & Faw, R. E. (2007). Fundamentals of Nuclear Science and Engineering. CRC Press.

dosimetry-measurement radiation-protection-optimization neutron-transport-calculation radiation-shielding-design monte-carlo-neutron-particle


Radiation Model

Radiation Model of Mobility and Migration

Radiation Law of Human Mobility
Parameter-free Mobility Model
Simini Radiation Model
Radyasyon Modeli

The Radiation Model, introduced by Simini et al. in 2012, is a parameter-free model for predicting human mobility and migration flows between geographic locations. Drawing an analogy from radiation physics, it predicts trip volumes based solely on population sizes at origin and destination, and the intervening population within the circle connecting them. It has been widely applied to commuting flows, migration, and epidemic spreading.

Parameter-free spatial interaction model

Population counts at origin and destination

Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484, 96–100.


Radiation Protection Optimization

Radiation Protection Optimization and Risk-Based Management

ALARA optimization
health physics planning
dose optimization

Health physics planning and optimization

Radiation protection optimization is a systematic approach to design and manage exposure reduction strategies using risk-benefit analysis, codified by the ICRP in the principle of As Low As Reasonably Achievable (ALARA) in 1977. By balancing radiation dose reduction against cost, effort, and societal benefit, it guides practical protection decisions in medical imaging, occupational settings, and environmental remediation.

Cember, H., & Johnson, T. E. (2009). Introduction to Health Physics (4th ed.). McGraw-Hill.

radiation-dose-assessment radiation-shielding-design dosimetry-measurement monte-carlo-neutron-particle reactor-kinetics


Radiation Shielding Design

Radiation Shielding Design and Optimization

shield analysis
attenuation design
dose reduction engineering

Radiation shielding design is an engineering discipline that uses physics-based calculations and materials selection to reduce radiation exposure to acceptable levels, originating from Curie and Rutherford's early radiation studies in the 1890s. By combining attenuation theory, source characterization, and dose modeling, it determines material composition, thickness, and geometry to protect workers, the public, and sensitive equipment.

International Commission on Radiation Units and Measurements (1993). Stopping Powers and Ranges for Protons and Alpha Particles. ICRU Report 49.

radiation-dose-assessment monte-carlo-neutron-particle neutron-transport-calculation criticality-safety-analysis dosimetry-measurement


Radiative Transfer

Radiative Transfer Modeling in Astrophysics

RT Modeling
Radiative Transport
Light Transport Simulation

Radiative transfer is the mathematical treatment of how light propagates through matter, including absorption, emission, and scattering. Central to astrophysics and stellar atmosphere modeling, radiative transfer calculations translate physical conditions (density, temperature, composition) into observable spectra and colors, bridging theory and observation.

Mihalas, D. (1978). Stellar Atmospheres (2nd ed.). San Francisco: W.H. Freeman.

Lucy, L. B. (1999). A Monte Carlo method for radiative transfer. Astrophysical Journal, 544(2), 889-906.

Robitaille, T. P., et al. (2011). YSO-VISION: self-consistent stellar atmosphere and disk modeling of young stellar objects. Astronomy & Astrophysics, 545, A47.


Radioactive Waste Classification

Radioactive Waste Classification and Characterization

waste categorization
hazard stratification
material disposition

International Atomic Energy Agency (IAEA)

Radioactive waste classification is a systematic framework for categorizing radioactive materials based on activity, heat generation, and long-term hazard potential, developed by the IAEA. It stratifies waste into classes (exempt, very low-level, low-level, intermediate-level, high-level) to determine appropriate management pathways—from near-surface disposal to deep geological repositories—ensuring environmental protection and regulatory compliance.

International Atomic Energy Agency (2009). Classification of Radioactive Waste. IAEA Safety Standards Series No. GSG-1.

U.S. Nuclear Regulatory Commission (2019). Waste Classification and Disposal. 10 CFR Part 61.

nuclear-decay-analysis radiation-dose-assessment radiation-protection-optimization criticality-safety-analysis dosimetry-measurement


Radiocarbon Dating

Radiocarbon dating is a radiometric technique that determines the age of organic materials by measuring the radioactive decay of ¹⁴C (carbon-14), a rare isotope produced in the atmosphere by cosmic ray interactions. Developed by Willard Libby in 1949, radiocarbon dating became a foundational method in archaeology, paleoclimate studies, and geology, enabling dating of organic materials from the past ~50,000 years with typical precision of ±50–100 years.

Libby, W. F. (1949). Radiocarbon dating. University of Chicago Press.

Reimer, P. J., et al. (2020). The IntCal20 radiocarbon calibration curve. Radiocarbon, 62(4), 725-757.


Radiographic Assessment in Veterinary Medicine

Systematic Radiographic Assessment and Diagnostic Imaging Interpretation in Veterinary Medicine

X-ray diagnosis
radiological assessment
radiographic interpretation

Radiographic assessment is a systematic diagnostic imaging method using X-rays to create two-dimensional images of internal structures, facilitating detection of skeletal, thoracic, and abdominal pathology. Since the discovery of X-rays in 1896 and their early adoption in veterinary medicine, radiography has remained foundational to veterinary diagnostics. Modern radiographic assessment combines conventional techniques with advanced processing and systematic interpretation protocols to maximize diagnostic accuracy and clinical impact.

Thrall, D. E. (Ed.). (2018). Textbook of Veterinary Diagnostic Radiology (7th ed.). St. Louis, MO: Elsevier.

Barr, F. J. (2005). Diagnostic Ultrasound in the Dog and Cat (2nd ed.). Edinburgh: Elsevier Saunders.

Busoni, F., Puchalski, S. M. (2011). Small animal ultrasonography: General principles, technique, and normal anatomy. In D. E. Thrall (Ed.), Textbook of Veterinary Diagnostic Radiology (6th ed., pp. 80-120). St. Louis, MO: Elsevier.


Radioimmunoassay

RIA
radioisotope immunoassay
isotope immunoassay
radioligand assay

Radioimmunoassay (RIA) is a highly sensitive, quantitative laboratory technique that measures the concentration of a specific antigen — such as a hormone, drug, or pathogen-derived protein — in a biological sample by exploiting competitive binding between a radiolabelled antigen and the sample antigen for a limited supply of specific antibody. Developed in the late 1950s, RIA is widely used in veterinary science, endocrinology, pharmacology, and clinical diagnostics.

Radioactivity counts (CPM/DPM), standard-curve concentration data

Yalow, R. S., & Berson, S. A. (1960). Immunoassay of endogenous plasma insulin in man. Journal of Clinical Investigation, 39(7), 1157–1175.

Sauer, M. J. (Ed.). (1981). Radioimmunoassay in Basic and Clinical Pharmacology. Springer.

enzyme-linked-immunosorbent-assay chemiluminescence-immunoassay western-blot flow-cytometry mass-spectrometry competitive-binding-assay


Radiomics

texture analysis
radiomics analysis
quantitative imaging biomarkers

Radiomics is a computational methodology that extracts large numbers of quantitative features from medical images (CT, MRI, PET) using automated image analysis and machine learning to discover imaging biomarkers associated with disease phenotype, prognosis, and treatment response. Developed by Lambin, Gillies, and colleagues in 2012, radiomics aims to decode the biology underlying visible imaging patterns, enabling personalized medicine through image-based phenotyping. It has emerged as a powerful tool in oncology for tumor characterization, prognosis prediction, and therapy response assessment.

Machine learning-based texture and morphology analysis

Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. Nature Reviews Clinical Oncology, 9(12), 676-684.

Gillies, R. J., Kinahan, P. E., Hricak, H. (2016). Radiomics: images are data. Radiology, 278(2), 563-577.

Kumar, V., Gu, Y., Basu, S., et al. (2012). Radiomics: the process and the challenges. Magnetic Resonance Imaging, 30(9), 1234-1248.

ct-iterative-reconstruction pet-kinetic-modeling quantitative-susceptibility-mapping dexa oct-angiography


RAFSI

RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) is a ranking multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Functional interval mapping (rank-reversal free)

Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I. (2020). Eliminating Rank Reversal Problem Using a New Multi-Attribute Model — The RAFSI Method. Mathematics

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Rainflow Counting

Rainflow counting is a fatigue cycle counting method that converts a complex stress history into individual cycles for damage assessment. Developed by Tatsuo Endo and colleagues in 1974, it provides the most physically realistic representation of fatigue damage when combined with Miner's linear cumulative damage hypothesis. The algorithm has become the industry standard in reliability engineering and vibration analysis.

Goodman, J. (1899). Mechanics Applied to Engineering. Longman, Green and Co.

Miner, M. A. (1945). Cumulative damage in fatigue. Journal of Applied Mechanics, 12(3), 159-164.

Endo, T., Matsumoto, T., Hasebe, T., & Mori, K. (1974). Damage evaluation of metals for random or varying loading. Proceedings of the Symposium on Mechanical Behavior of Materials.

ASTM International (2021). E1049-21: Standard Practices for Cycle Counting in Fatigue Analysis.


RAM

RAM (Root Assessment Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Sotoudeh-Anvari, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Power-function aggregation with benefit/cost separation

Sotoudeh-Anvari, A. (2023). Root Assessment Method (RAM): A novel multi-criteria decision making method and its applications in sustainability challenges. Journal of Cleaner Production


Raman Deconvolution

Raman Spectroscopy Deconvolution Analysis

Raman deconvolution
Raman peak fitting
spectral analysis

Raman Deconvolution is the mathematical decomposition of experimental Raman spectra into constituent peaks using spectral fitting algorithms. Building on Raman spectroscopy (discovered by C.V. Raman in 1928), Raman deconvolution resolves overlapping vibrational bands into individual component peaks, revealing detailed information about molecular bonds, crystal phases, strain, and defects. This quantitative analysis transforms raw Raman spectra into actionable chemical and structural insights, making it essential for materials characterization, quality control, and scientific discovery.

Raman, C. V., & Krishnan, K. S. (1928). The scattering of light by molecules. Nature, 121(3048), 501-502.

Srivastava, A., Jain, R., & Gupta, A. (2014). Raman spectroscopy as a tool for quality assessment in polymeric materials. Advanced Materials & Processes, 195(3), 6-13.

Ferraro, J. R., Nakamoto, K., & Brown, C. W. (2003). Introductory Raman Spectroscopy (2nd ed.). Academic Press.