PedsQL Sickle Cell Module

Pediatric Quality of Life Inventory—Sickle Cell Disease Module

hematologic disease pediatric quality of life

The PedsQL Sickle Cell Disease Module is a disease-specific instrument developed by Varni et al. in 2012 to measure quality of life in children and adolescents with sickle cell disease aged 2–18 years. Measuring across domains including pain and symptoms, functional limitations, school impact, and disease-related worry, it captures how sickle cell disease and its complications affect daily life and well-being. Used alongside the PedsQL Generic Core Scales, it provides comprehensive assessment of sickle cell disease-specific and general health-related quality of life.

Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2012). The PedsQL in pediatric sickle cell disease: Measurement model, factor structure, and reliability and validity of the pediatric quality of life inventory sickle cell disease module. Journal of Health Psychology, 17(7), 1012-1024.

Klingel, M., Kamps, R., Douwes Dekker, H. M., Grobbee, D. E., Hartman, A., & Moll, A. C. (2012). Sickle cell disease and quality of life. International Journal of Environmental Research and Public Health, 15(8), 1638.

pedsql-diabetes pedsql-cancer pedsql-cardiac paqlq


Peer Learning Scale

Various; based on collaborative learning theory

The Peer Learning Scale measures the extent and quality of collaborative learning experiences among students, capturing the frequency of peer interaction, perceived support from peers, quality of peer feedback, and learning gains from collaboration. Grounded in social-constructivist theory and decades of research on collaborative learning, the PLS assesses a critical dimension of the modern learning environment: peer interaction is not incidental but a core mechanism of learning through explanation, feedback, and distributed cognition.

Self-report questionnaire or observation

Topping, K. J. (2009). Peer assessment. Theory into Practice, 48(1), 20–27.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

classroom-environment-scale academic-resilience-scale study-skills-assessment academic-help-seeking-scale university-student-satisfaction


Peer Review Process

Peer Review Process in Academic Publishing

Peer Reviewing
Manuscript Evaluation
Scholarly Review

Scientific publishing community; formalized by journals and COPE

Peer review is the process by which manuscripts are evaluated by experts in the same field before publication in academic journals. Reviewers assess the manuscript's scientific merit, methodology, clarity, and contribution to the field. Established in 1665 with the first scientific journal (Philosophical Transactions of the Royal Society), peer review remains the gold standard for quality control in academic publishing. Despite ongoing criticism and proposals for alternatives, peer review continues to filter low-quality and unethical work, though it is imperfect and sometimes slow.

Committee on Publication Ethics (2023). COPE Guidelines: Ethical Guidelines for Peer Reviewers. COPE.

Shamoo, A. E., & Resnik, D. B. (2009). Responsible Conduct of Research (2nd ed.). Oxford University Press.

Bailey, J. H., Mehlman, M. J., Rath, D. P., & Garrison, R. H. (2006). Perspectives on Peer Review. Journal of Scholarly Publishing, 37(2), 120–135.

icmje-authorship-criteria plagiarism-in-research cope-guidelines retraction-process


Pelvic Floor Distress Inventory

The PFDI is a condition-specific quality-of-life measure designed to assess symptom distress across the spectrum of pelvic floor disorders, including urinary incontinence, pelvic organ prolapse, and fecal incontinence. Originally published by Barber and colleagues in 2001 with 93 items, the 20-item short form (PFDI-20) was later developed to improve clinical feasibility while maintaining measurement precision. It is now the standard outcome measure in pelvic floor disorder research and clinical practice.

Barber, M. D., Kuchibhatla, M. N., Pieper, C. F., & Bump, R. C. (2001). Psychometric evaluation of 2 comprehensive condition-specific quality of life instruments for women with pelvic floor disorders. American Journal of Obstetrics and Gynecology, 185(6), 1388–1395.

Barber, M. D., Walters, M. D., & Bump, R. C. (2005). Short forms of two condition-specific quality-of-life questionnaires for women with pelvic floor disorders (PFDI-20 and PFIQ-7). American Journal of Obstetrics and Gynecology, 193(1), 103–113.


Penetration Testing Methodology

Systematic Penetration Testing Framework and Exploitation Methodology

Pen Testing
Ethical Hacking
Security Testing

National Institute of Standards and Technology (NIST), OWASP

Penetration testing is an authorized, controlled simulated attack on systems, networks, and applications to evaluate their security defenses. Unlike vulnerability assessment (which identifies weaknesses), penetration testing actively exploits vulnerabilities to demonstrate real-world impact, confirm exploitability, and assess an organization's incident response capabilities.

Authorized security exploit and assessment

National Institute of Standards and Technology (2008). Penetration Testing and Security Testing. NIST Special Publication 800-115.

OWASP (2023). OWASP Testing Guide v4.2. OWASP Foundation.

Tenable (2023). Nessus Professional: Automated Vulnerability Assessment and Exploitation. Technical Report.


Peng-Robinson Equation of State

Peng-Robinson Cubic Equation of State for Fluids

The Peng-Robinson equation of state is a cubic model that describes the thermodynamic properties of pure fluids and mixtures. Introduced by Ding-Yu Peng and David Bernard Robinson in 1976, it improves upon earlier models (van der Waals, Redlich-Kwong) by better predicting compressibility factors and phase equilibria, especially near the critical point. It is widely used in petroleum engineering, chemical process design, and natural gas calculations.

Equation of state; thermodynamic property correlation

Peng, D. Y., & Robinson, D. B. (1976). A new two-constant equation of state. Industrial & Engineering Chemistry Fundamentals, 15(1), 59-64.

Reid, R. C., Prausnitz, J. M., & Sherwood, T. K. (1987). The Properties of Gases and Liquids (4th ed.). McGraw-Hill.

Soave, G. (1972). Equilibrium constants from a modified Redlich-Kwong equation of state. Chemical Engineering Science, 27(6), 1197-1203.

unifac pinch-analysis cstr-model pfr-model


Penman-Monteith Equation

Penman-Monteith Equation for Evapotranspiration

PM Equation
FAO-56 PM
Evapotranspiration Model

The Penman-Monteith equation is a mechanistic model for estimating evapotranspiration (ET), the combined loss of water from soil and plant canopies to the atmosphere. First proposed by Penman (1948) for bare soil and water surfaces, then extended by Monteith (1965) to incorporate plant resistance to water vapor diffusion, it has become the international standard for water balance studies, crop water requirement calculation, and hydrological modeling.

Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society A, 193(1032), 120-145.

Monteith, J. L. (1965). Evaporation and environment. Symposia of the Society for Experimental Biology, 19, 205-234.

Allen, R. G., Pereira, L. S., Raes, D., Smith, M., & Hargreaves, G. H. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, Rome: FAO.


Penn State Worry Questionnaire

Thomas J. Meyer, Marta L. Miller, Robin L. Metzger, Thomas D. Borkovec

The Penn State Worry Questionnaire (PSWQ) is a 16-item self-report instrument specifically designed to measure the trait dimension of worry—the tendency to worry excessively across situations. Developed by Meyer, Miller, Metzger, and Borkovec in 1990, the PSWQ has become the standard instrument for assessing worry as a transdiagnostic symptom dimension in clinical and research settings.

Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy, 28(6), 487-495.


PORAS

PORAS
Perceived Organizational Readiness
Perceived Readiness Scale

Christopher D. Helfrich, PhD; Ying-Fang Li, PhD; Neil D. Sharp, MD; colleagues at Veterans Affairs and University of Washington

The Perceived Organizational Readiness for Assisting the System (PORAS) is a 19-item self-report measure developed by Helfrich and colleagues to assess organizational readiness to implement health information technology systems and other healthcare innovations. Grounded in Weiner's theory of organizational readiness for change, the PORAS measures four dimensions of readiness: Valence (perceived importance of the change to the organization), Motivation (organizational commitment and drive to implement), Resource Adequacy (availability of financial, human, and technical resources), and Change Efficacy (staff belief in organizational capability to successfully implement). While originally developed for health IT implementation, the PORAS framework and scale are applicable to broader healthcare innovations and evidence-based practice implementation.

Helfrich, C. D., Li, Y. F., Sharp, N. D., & Sales, A. E. (2009). Organizational readiness to change assessment (ORCA): Development of an instrument based on the perspectives of health care professionals. Journal of the American Medical Informatics Association, 16(4), 523–530.

Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4, 67.

organisational-readiness-change evidence-based-practice-attitude implementation-climate-scale implementation-leadership-scale knowledge-to-action-scale


Perceived Organizational Support Scale

POSS
POS Scale
Eisenberger Organizational Support

The Perceived Organizational Support Scale (POSS) measures employees' beliefs about the degree to which their employing organization values their contributions and cares about their well-being. Developed by Eisenberger and colleagues in 1986, it is a foundational construct in organizational psychology that predicts employee engagement, commitment, and performance. The scale is grounded in social exchange theory and reciprocity norms.

Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507.

Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87(4), 698–714.

Caesens, G., & Stinglhamber, F. (2014). The relationship between perceived organizational support and work engagement: The role of self-efficacy and its outcomes. European Review of Applied Psychology, 64(5), 259–267.

leader-member-exchange-scale psychological-capital-questionnaire organizational-commitment-questionnaire proactive-personality-scale occupational-stress-index


Perceived Stress Reactivity Scale

Perceived Stress Reactivity Scale (PSRS)

Individual differences in stress responsiveness and vulnerability

The PSRS is an 8-item self-report scale measuring individual differences in perceived reactivity to stressful situations—the subjective sense of being easily stressed, emotionally reactive, or overwhelmed by demands. Developed by Hewitt and colleagues in the context of perfectionism and stress research, the PSRS captures a trait-like tendency toward heightened stress reactivity, often termed stress sensitivity or emotional reactivity. The scale is used in clinical and research settings to identify individuals at risk for stress-related psychopathology and to measure changes in stress responsiveness over time.

Hewitt, P. L., Flett, G. L., Mikail, S. F., & Singh, R. (2016). Perfectionism and stress processes in psychopathology. In G. L. Flett & P. L. Hewitt (Eds.), Perfectionism and psychological distress (pp. 255-284). Springer.

Eisler, I. (2009). The empirical and theoretical base of family therapy and multiple family day treatment for adolescent anorexia nervosa. Journal of Family Therapy, 17(3), 353-372.


Perceived Stress Scale

Sheldon Cohen, Tom Kamarck, and Robin Mermelstein

The Perceived Stress Scale (PSS), developed by Cohen, Kamarck, and Mermelstein in 1983, is the most widely used measure of subjective stress in research and clinical practice. Available in 10-item (PSS-10) and 14-item (PSS-14) versions, the PSS assesses the degree to which individuals perceive situations as unpredictable, uncontrollable, and overwhelming. The scale captures stress as a result of how people interpret and react to life events rather than the events themselves.

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385-396.

Cohen, S., & Williamson, G. (1994). Perceived stress in a probability sample of the United States. In S. Spacapan & S. Oskamp (Eds.), The social psychology of health: Claremont Symposium on Applied Social Psychology (pp. 31-67). Thousand Oaks, CA: SAGE Publications.

job-demands-resources-scale emotional-exhaustion-scale work-ability-index organizational-commitment-scale job-satisfaction-survey


Perceived Value Scale for Tourism

The Perceived Value Scale for Tourism (PVST) measures visitors' judgments of whether tourism experiences deliver fair value—balancing perceived benefits (quality of experience, emotional satisfaction, novelty) against perceived costs (monetary price, time investment, effort). Rooted in Zeithaml's value perception theory (1988) and extended by Petrick (2002) to leisure contexts, the PVST operationalizes value as multidimensional (not price alone), capturing emotional and relative components alongside financial fairness. Value perception is a critical satisfaction driver and predictor of repeat visitation and word-of-mouth, particularly for experiences with high upfront investment and uncertain return.

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.

Williams, M. R., & Attaway, J. S. (2001). Exploring salespersons' customer orientation as a mediator of organizational culture's influence on buyer-seller relationships. Journal of Personal Selling and Sales Management, 16(4), 33-52.

Petrick, J. F. (2002). Development of a multi-dimensional scale for measuring the perceived value of a service. Journal of Leisure Research, 34(2), 119-134.

Sanchez, J., Callarisa, L., Rodríguez, R. M., & Moliner, M. A. (2006). Perceived value of the purchase of a tourism product. Tourism Management, 27(3), 394-409.

tourist-satisfaction-scale hotel-service-quality-scale destination-image-scale travel-motivation-scale tourist-loyalty-scale


Perceptual and Preference Mapping

Perceptual Mapping
Preference Mapping
Attribute-Based Mapping
Algısal Haritalama

Perceptual and preference mapping is a family of multivariate techniques that simultaneously positions competing objects—brands, products, or stimuli—and respondent preferences within a common low-dimensional space. Introduced systematically by Hauser and Koppelman (1979), the approach lets researchers visualize how consumers perceive attribute-level similarities among objects and which attributes drive individual or segment-level choice. It is widely used in market research, sensory science, and strategic positioning analysis.

Attribute ratings or similarity judgments

Output: Low-dimensional joint space of objects and preferences

Hauser, J. R., & Koppelman, F. S. (1979). Alternative perceptual mapping techniques: Relative accuracy and usefulness. Journal of Marketing Research, 16(4), 495–506.


GRBAS Voice Perceptual Scale

The GRBAS Scale (Grade, Roughness, Breathiness, Asthenia, Strain) is a clinician-rated perceptual assessment tool for classifying voice quality across five distinct vocal dimensions. Developed by Hirano in 1981, GRBAS provides a standardized language for voice clinicians and physicians to describe dysphonia characteristics (e.g., rough voice, breathy voice, weak voice) using ordinal subscales. GRBAS is foundational in voice pathology education and remains widely used in clinical and research settings despite modern objective measures like acoustic analysis and laryngeal imaging.

Hirano, M. (1981). Clinical Examination of Voice. Vienna: Springer-Verlag.

Debruyne, F., Decoster, W., Van Gorp, G., & Verheggen, R. (1997). Perceptual Evaluation of Voice Disorders. Acta Oto-Laryngologica, 117(S527), 34–38.

Karnell, M. P., Melton, S. D., Childers, D. G., & Hicks, D. M. (1991). Use of Nasofiberscope and Stroboscopy in Teaching Perceptual Voice Evaluation. Journal of Voice, 5(3), 236–241.


Perinatal Anxiety Screening Scale

The Perinatal Anxiety Screening Scale (PASS) is a 31-item self-report instrument designed to screen for anxiety symptoms during pregnancy and the postpartum period. Developed by Somerville and colleagues in 2014, it addresses the clinical need for a brief, validated tool that captures the full spectrum of perinatal anxiety disorders, including generalized anxiety, panic, obsessive-compulsive features, and posttraumatic stress in the context of childbirth.

Somerville, S., Dedman, K., Hagan, R., Oxnam, E., Wettinger, M., Byrne, S., Coo, S., Doherty, D. A., & Papagaroufalis, K. (2014). The Perinatal Anxiety Screening Scale (PASS): development and preliminary validation. Archives of Women's Mental Health, 17(5), 443-454.


Periodontal Probing

probing depth measurement
pocket depth assessment

Periodontal probing is a clinical assessment technique that measures the depth of gingival crevices and periodontal pockets to diagnose periodontal disease. Introduced by the American Academy of Periodontology in the mid-20th century, it remains the gold standard for assessing periodontal health status. The procedure evaluates the clinical attachment level and recession depth to identify inflammation, attachment loss, and disease progression.

Armitage, G. C. (1999). Development of a classification system for periodontal diseases and conditions. Annals of Periodontology, 4(1), 1-6.

Jeffcoat, M. K. (1992). The etiology and pathogenesis of periodontal diseases are multifactorial. The Journal of the American Dental Association, 123(5), 85-89.

Page, R. C., & Kornman, K. S. (1997). The pathogenesis of human periodontitis: an introduction. Periodontology 2000, 14(1), 9-11.

dmft-index gingival-index dental-erosion-index bone-density-dental tooth-mobility-assessment


Peritraumatic Distress Inventory

The PDI is a 13-item self-report measure assessing the emotional, physical, and cognitive distress experienced during or immediately after a traumatic event. Developed by Brunet, Akerib, and Birmes in 2001, it captures acute peritraumatic responses (dissociation, fear, confusion) that predict risk for chronic PTSD. It is widely used in emergency medicine, military medical systems, and trauma research to identify acutely traumatized individuals at high risk for persistent psychological injury.

Brunet, A., Akerib, V., & Birmes, P. (2001). Don't forget initial symptoms of acute stress disorder: Evaluation of a simple stack of criteria. Journal of Nervous and Mental Disease, 189(7), 460-466.

Birmes, P., Brunet, A., Carreno, I., Ducassé, J. L., Charlet, J. P., Lauque, D., ... & Schmitt, L. (2003). The predictive power of peritraumatic dissociation and acute stress symptoms for posttraumatic stress symptoms: A three-month follow-up study. Journal of Nervous and Mental Disease, 191(5), 300-304.

pcl-military combat-exposure-scale post-deployment-reintegration soldier-adaptation-measure


PERMA Profiler

The PERMA-Profiler is a 23-item multidimensional measure of flourishing developed by Butler and Kern in 2016 based on Seligman's PERMA model of positive psychology. It assesses five core domains of human flourishing—Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment—plus Negative Emotion and Loneliness as contextual factors. This instrument bridges the theoretical PERMA framework with practical measurement, enabling comprehensive assessment of psychological well-being across multiple life dimensions.

Butler, J., & Kern, M. L. (2016). The PERMA-Profiler: A brief multidimensional measure of flourishing. International Journal of Wellbeing, 6(3), 1–48.

flourishing-scale who-5-wellbeing-index positive-mental-health-scale meaning-in-life-questionnaire


Permutation Test

randomization test
exact permutation test
re-randomization test
Permütasyon Testi

Good (2005); Edgington & Onghena (2007); resampling tradition

The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.

Empirical permutation distribution of the test statistic

Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer.

Edgington, E. S., & Onghena, P. (2007). Randomization Tests (4th ed.). CRC Press.

bootstrap-inference jackknife trimmed-mean-test robust-correlation winsorized-estimation


Persistent Homology

Persistent Homology (Topological Data Analysis)

Topological Persistence
Persistence Barcodes
Persistent Betti Numbers
Kalıcı Homoloji

Persistent homology is a method in topological data analysis that quantifies the multi-scale topological structure of data by tracking connected components, loops, and voids as a scale parameter varies. Introduced by Edelsbrunner, Letscher, and Zomorodian in 2002, it encodes topological features through their birth and death scales, producing persistence diagrams or barcodes that serve as compact, coordinate-free descriptors of shape. The approach is robust to noise and provides a mathematically rigorous bridge between discrete data and algebraic topology.

Topological feature extraction algorithm

Output: Persistence diagrams / barcodes

Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2002). Topological persistence and simplification. Discrete & Computational Geometry, 28(4), 511–533.

Carlsson, G. (2009). Topology and data. Bulletin of the American Mathematical Society, 46(2), 255–308.


Person-Centred Care Assessment Tool

The Person-Centred Care Assessment Tool (PCAT) is an observational and staff-report instrument designed to evaluate the degree to which healthcare services and interactions embody person-centered care principles. Developed by Brendan McCormack and David Edvardsson, the PCAT assesses key dimensions of person-centered practice: knowing the person, being respectful, engaging authentically, taking a holistic view, and adapting care to individual values and preferences. The tool has been widely used in nursing homes, dementia care, hospital wards, and community health settings to evaluate care environment quality and identify opportunities for person-centered transformation.

McCormack, B., Eley, D., Prideaux, D., & Jackson, D. (2010). Blending critical realism and hermeneutics in a PhD research: Researching person-centred care. Qualitative Research Journal, 10(1), 42-54.

Edvardsson, D., Winblad, B., & Sandman, P. O. (2008). Person-centred care of people with severe Alzheimer's disease: current status and ways forward. The Lancet Neurology, 7(4), 362-367.

collaboste-scale patient-enablement-instrument care-transitions-measure trust-in-physician-scale


QPR
Neil-QPR
Process of Recovery Questionnaire

Neil, S. T., Kilbride, M., Pitt, L., et al.

The Questionnaire about the Process of Recovery (QPR), also called the 'Neil-QPR,' is a 22-item self-report measure assessing subjective recovery processes in individuals with serious mental illness, particularly schizophrenia and related disorders. Developed by Stephen T. Neil, Matthias Kilbride, Leonie Pitt, and colleagues in 2009, the QPR captures dimensions central to lived experience of recovery: awareness of mental illness and strengths, motivation to pursue recovery goals, effective coping strategies, hope, and self-esteem. Unlike scales measuring recovery outcomes, the QPR emphasizes recovery as an active process—the psychological and behavioral work individuals undertake.

Neil, S. T., Kilbride, M., Pitt, L., Nothard, S., Welford, P., Sellwood, W., & Bebbington, P. (2009). The questionnaire about the process of recovery (QPR): A measurement tool developed in collaboration with service users. Schizophrenia Bulletin, 35(2), 403-413.

recovery-assessment-scale mental-health-recovery-measure empowerment-scale-rogers personal-recovery-questionnaire


MMPI Personality Assessment

Minnesota Multiphasic Personality Inventory Assessment

Starke R. Hathaway, J. Charnley McKinley

The Minnesota Multiphasic Personality Inventory (MMPI) is a 567-item standardized self-report inventory designed to assess personality traits, psychopathology, and behavioral tendencies in adults. Originally published in 1943 and revised as the MMPI-2 in 1989 and the MMPI-2-RF in 2008, the MMPI remains the most widely used and researched objective personality assessment instrument in clinical, forensic, and industrial settings.

Objective standardized personality inventory

Butcher, J. N., Dahlstrom, W. G., Graham, J. R., Tellegen, A., & Kaemmer, B. (2001). MMPI-2: Manual for administration, scoring, and interpretation (Rev. ed.). University of Minnesota Press.

Ben-Porath, Y. S., & Tellegen, A. (2008). MMPI-2-RF: Manual for administration, scoring, and interpretation. University of Minnesota Press.


Pesaran CD Test

Pesaran Cross-Sectional Dependence (CD) Test

CD Test
Cross-Sectional Dependence Test
Pesaran General CD Test
Kesitsel Bağımlılık Testi

The Pesaran CD test is a general diagnostic procedure for detecting cross-sectional dependence in panel data models. Developed by M. Hashem Pesaran (2021), it is applicable to both balanced and unbalanced panels with large N and T, and retains validity under heterogeneous slope coefficients. The test is widely adopted in empirical economics, finance, and political economy as a prerequisite check before selecting appropriate estimators or unit-root tests for panel datasets.

Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.


Pesaran-Timmermann Test

Pesaran-Timmermann Test of Directional Predictive Accuracy

Nonparametric Predictive Performance Test

Introduced by Pesaran and Timmermann (1992), the PT test is a nonparametric procedure that evaluates whether a forecasting model correctly predicts the direction (sign) of a target variable more often than would be expected by chance. It is widely used in financial econometrics and macroeconomic forecasting to assess the practical utility of a model beyond simple error metrics, particularly when the economic cost of getting the direction wrong is high.

Forecasts and realizations are independently distributed (no directional predictability)

Pesaran, M. H., & Timmermann, A. (1992). A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics, 10(4), 461–465.


Pesticide Efficacy Trial

Field Trial Design and Efficacy Assessment for Pest Control Products

Fungicide efficacy test
Insecticide trial
Disease control evaluation

EPPO (European and Mediterranean Plant Protection Organization)

Pesticide Efficacy Trial is an experimental design and analysis pipeline for evaluating the effectiveness of fungicides, insecticides, and other plant protection products under field or greenhouse conditions. Standardized by EPPO and IOBC, this method quantifies pest or disease control and informs regulatory approval, product comparison, and farmer decision-making.

European and Mediterranean Plant Protection Organization (2010). Standard procedures for efficacy evaluation of plant protection products. EPPO Bulletin 40(3), 313-328.

International Organization for Biological Control (2007). Quality control of biological pest control agents. IOBC/WPRS Bulletin 30(5), 1-54.

weed-density-mapping crop-growth-simulation crop-yield-estimation nitrogen-use-efficiency phenological-observation


PET Kinetic Modeling

Positron Emission Tomography Kinetic Modeling

PET pharmacokinetics
Dynamic PET
PET compartmental modeling

PET kinetic modeling is a quantitative analysis technique that tracks the temporal behavior of radioactive tracers in tissue to extract physiological parameters such as blood flow, metabolic rate, and receptor density. Established by Patlak, Logan, and Gunn in the 1980s and 1990s, kinetic modeling transforms raw PET time-activity curves into interpretable biological measures. It is widely used in neurology, oncology, and cardiology to assess disease severity, treatment response, and regional tissue function.

Mathematical framework for tracer kinetics in PET imaging

Patlak, C. S., Blasberg, R. G., Fenstermacher, J. D. (1983). Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Journal of Cerebral Blood Flow & Metabolism, 3(1), 1-7.

Logan, J., Fowler, J. S., Christman, D. R., et al. (2000). Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C]cocaine PET studies in human subjects. Journal of Cerebral Blood Flow & Metabolism, 10(5), 740-747.

Gunn, R. N., Gunn, S. R., Cunningham, V. J. (2001). Positron emission tomography compartmental models. Journal of Cerebral Blood Flow & Metabolism, 21(6), 635-652.

oct-angiography dti-tractography quantitative-susceptibility-mapping ct-iterative-reconstruction radiomics


Petrographic Analysis

microscopy analysis
thin section analysis
modal composition determination

Petrographic analysis is the microscopic examination of rock thin sections to determine mineral composition, grain size, texture, and diagenetic alteration. Pioneered by Sorby in 1858, this method remains the gold standard for understanding lithology and has evolved to include quantitative image analysis and cathodoluminescence. Petrographic data anchor well-log interpretation, validate seismic velocity models, and constrain paleoenvironmental and diagenetic histories.

Tucker, M. E. (2003). Sedimentary Rocks in the Field: A Color Guide (3rd ed.). John Wiley & Sons.

Shelley, D. (1985). Diagenesis of Shales and Competent Interbeds: Petrographic and Geochemical Evidence. Journal of the Geological Society, 142(6), 1003–1021.

Folk, R. L. (1954). The distinction between grain size and mineral composition in sedimentary rock nomenclature. Journal of Geology, 62(4), 344–359.

well-log-analysis stratigraphic-correlation geologic-mapping rock-mass-classification basin-subsidence-analysis


PF-ARAS

PF-ARAS (Pythagorean extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)

Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


PF-COCOSO

PF-COCOSO (PF-CoCoSo — Pythagorean extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-CODAS

PF-CODAS (Pythagorean extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-COPRAS

PF-COPRAS (Pythagorean extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-EDAS

PF-EDAS (Pythagorean extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-ELECTRE-II

PF-ELECTRE-II (Pythagorean fuzzy ELECTRE-II for group MCDM) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M.; Ilyas, F.; Garg, H. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pythagorean outranking — Pythagorean Fuzzy Number (PFN: μ, ν, π; μ²+ν² ≤ 1)

Akram, M., Ilyas, F., Garg, H. (2021). ELECTRE-II method for group decision-making in Pythagorean fuzzy environment. Applied Intelligence

pf-ahp pf-swara pf-bwm pf-dematel pf-entropy electre-ii fuzzy-electre-ii


PF-GRA

PF-GRA (Pythagorean extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-MABAC

PF-MABAC (Pythagorean extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-MARCOS

PF-MARCOS (Pythagorean extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-MOORA

PF-MOORA (Pythagorean extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-PROMETHEE

PF-PROMETHEE (Pythagorean extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-SAW

PF-SAW (Pythagorean extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-TODIM

PF-TODIM (Pythagorean extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Ren, P., Xu, Z., Gou, X. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Ren, P., Xu, Z., Gou, X. (2016). Pythagorean Fuzzy TODIM Approach to Multi-Criteria Decision Making. Applied Soft Computing


PF-TOPSIS

PF-TOPSIS (Pythagorean extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhang, X., Xu, Z. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zhang, X., Xu, Z. (2014). Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets. International Journal of Intelligent Systems


PF-VIKOR

PF-VIKOR (Pythagorean extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Abbas, S., Hussain, Z., Hussain, Z., Ali, I., Mudabar, S.M. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Abbas, S., Hussain, Z., Hussain, Z., Ali, I., Mudabar, S.M. (2024). Advanced Entropy Models in Pythagorean Fuzzy Sets: Revolutionizing Multi-Criteria Decision Making with PF-VIKOR. Fuzzy Economic Review


PF-WASPAS

PF-WASPAS (Pythagorean extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PF-WPM

PF-WPM (Pythagorean extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PFR Model

ideal tubular reactor
plug-flow model
PFR

The PFR (Plug Flow Reactor) model describes the behavior of a tubular reactor in which fluid elements move through as distinct plugs with no axial mixing. Fluid at the inlet is freshly unreacted; as it travels downstream, reactions progress. This idealized model, formalized by Octave Levenspiel alongside CSTR theory, is the opposite extreme: while CSTRs are fully mixed, PFRs have no axial mixing. In practice, PFRs achieve higher conversion than CSTRs for the same residence time and are widely used in the chemical and petroleum industries.

Mathematical model for plug-flow reactor

Levenspiel, O. (1999). Chemical Reaction Engineering (3rd ed.). John Wiley & Sons.

Fogler, H. S. (2016). Elements of Chemical Reaction Engineering (5th ed.). Pearson.

Schmidt, L. D. (2005). The Engineering of Chemical Reactions (2nd ed.). Oxford University Press.


Pharmacokinetic Compartment Model

Mammillary Compartment Model
Multi-Compartment PK Model
Compartmental Analysis
Farmakokinetik Kompartman Modeli

The pharmacokinetic compartment model represents the body as one or more hypothetical compartments interconnected by first-order rate processes, describing how a drug is absorbed, distributed, and eliminated over time. Systematized by Gibaldi and Perrier in 1982, these models use ordinary differential equations to characterize plasma concentration-time profiles. They are the cornerstone of drug development, dosage regimen design, and regulatory submission pharmacokinetic analyses.

Deterministic ODE-based pharmacokinetic model

Nonlinear least squares or maximum likelihood

2 (one-compartment) to 6+ (two-compartment)

Gibaldi, M., & Perrier, D. (1982). Pharmacokinetics (2nd ed.). Marcel Dekker.


Pharmacophore Modeling

Pharmacophore-based Ligand Design and Virtual Screening

pharmacophore pattern recognition
3D pharmacophore

Pharmacophore modeling identifies the spatial arrangement of molecular features (hydrogen bond donors, acceptors, aromatic rings) that are essential for biological activity. Introduced by Gund in 1977, this ligand-based method creates a three-dimensional pattern that can screen chemical libraries and design new active compounds without requiring receptor structure.

Pattern-based virtual screening pipeline

Wermuth, C. G., Ganellin, C. R., Lindberg, P., & Mitscher, L. A. (1998). Glossary of terms used in medicinal chemistry. Pure and Applied Chemistry, 70(5), 1129-1143.

Ohno, K. & Ueda, Y. (2006). Modern photochemistry of organic compounds. Wiley & Sons.

Leung, S. C., Bodkin, M., von Delft, F., & Morris, G. M. (2012). SiteMap: a tool for identifying and characterizing binding sites in protein structures. Journal of Chemical Information and Modeling, 52(11), 3008-3020.


Pharmacovigilance PRR/ROR

Proportional Reporting Ratio and Reporting Odds Ratio for Pharmacovigilance

PRR
ROR
signal detection
adverse event monitoring

Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR) are statistical methods for detecting safety signals in spontaneous adverse event reporting databases. Developed and formalized by researchers in the early 2000s, these measures identify drug-adverse event associations that warrant further investigation.

Szarfman, A., Tonning, J. M., Doraiswamy, P. M., & Osgood, D. J. (2002). Pharmacovigilance in the post-marketing setting: establishing causal links between drugs and adverse events. Drug Safety, 25(9), 619-631.

van Puijenbroek, E. P., Bate, A., Leufkens, H. G., Lindquist, M., Orre, R., & Egberts, A. C. (2002). A comparison of measures of disproportionality for signal detection in spontaneous adverse drug reaction reporting. Pharmacoepidemiology and Drug Safety, 11(1), 3-10.


Phase-Field Modeling

phase-field method
diffuse interface method

Phase-Field Modeling (PFM) is a continuum computational method for simulating microstructure evolution, phase transitions, and interfacial dynamics without explicitly tracking moving boundaries. Developed from Cahn-Ginzburg-Landau theory in the 1950s, PFM represents distinct phases through continuous order parameters that vary smoothly over diffuse interfaces. This approach elegantly handles topological changes (nucleation, coalescence, pinch-off), complex interface geometries, and strongly coupled multiphysics. It is the dominant method for studying dendritic growth, spinodal decomposition, grain evolution, and reactive transport in materials science.

Cahn, J. W. (1958). Free energy of a nonuniform system: Interfacial free energy. The Journal of Chemical Physics, 28(2), 258-267.

Ginzburg, V. L., & Landau, L. D. (1950). Theory of superconductivity. Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, 20, 1064.

Wang, S. L., Sekerka, R. F., Wheeler, A. A., Murray, B. T., Coriell, S. R., Braun, R. J., & McFadden, G. B. (2010). Thermodynamically-consistent phase-field models for solidification. Physica D, 69(3-4), 189-200.


Phase I Clinical Trial

Phase I Clinical Trial (First-in-Human / Dose-Escalation Study)

Phase 1 trial
first-in-human study
FIH study
dose-escalation study

1960s (formal regulatory framework established ~1963–1970s)

Regulatory and clinical pharmacology community; formalized in U.S. FDA IND regulations (1963) and ICH guidelines

A Phase I clinical trial is the first stage of human testing for a new drug, biologic, or intervention. Its primary objective is to evaluate safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) rather than therapeutic efficacy. Small cohorts of participants — typically healthy volunteers or patients with advanced disease — receive sequentially increasing doses to identify the maximum tolerated dose (MTD) and the dose-limiting toxicities (DLTs) that define the boundary for subsequent trials.

Safety events, pharmacokinetic (PK) measurements, dose-level assignments, adverse event grading (CTCAE)

Storer, B. E. (1989). Design and analysis of phase I clinical trials. Biometrics, 45(3), 925–937.

International Council for Harmonisation (ICH). (2016). ICH E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1). ICH Harmonised Guideline.

phase-ii-clinical-trial phase-iii-clinical-trial randomized-clinical-trial dose-response-analysis adaptive-randomized-clinical-trial diagnostic-accuracy-study


Phase II clinical trial

Phase 2 trial
Phase II study
early efficacy trial
proof-of-concept trial

1960s–1970s (formalised in US federal drug regulation)

U.S. Food and Drug Administration / ICH E8 guidelines (institutionalised framework)

A Phase II clinical trial is the second stage in the drug or intervention development pipeline, conducted after Phase I safety testing. Its primary goal is to assess whether the intervention shows preliminary efficacy signals in a relevant patient population at the dose established in Phase I, while continuing to characterise the safety and tolerability profile. Phase II trials are generally smaller than Phase III confirmatory trials and serve as critical go/no-go decision points before large-scale investment.

Outcome measurements (response rate, biomarkers, adverse events), patient-level longitudinal data

Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.

phase-i-clinical-trial phase-iii-clinical-trial randomized-clinical-trial dose-response-analysis adaptive-randomized-clinical-trial diagnostic-accuracy-study


Phase III clinical trial

Phase 3 trial
confirmatory trial
pivotal trial
Phase III RCT

1962 (Kefauver-Harris Amendment formalised phased drug development)

FDA regulatory framework / ICH guidelines

A Phase III clinical trial is a large-scale, confirmatory randomised controlled trial designed to establish the efficacy and safety of an intervention in the target patient population before regulatory submission. It builds on the signal identified in Phase II, tests the intervention at its proposed dose under controlled conditions, and provides the primary evidence base for marketing authorisation or guideline adoption.

Confirmatory randomised controlled trial

Clinical outcome data (efficacy endpoints, safety events, patient-reported outcomes)

International Council for Harmonisation (ICH). (1997). E8 General Considerations for Clinical Trials. ICH Harmonised Guideline.

phase-ii-clinical-trial phase-iv-study randomized-clinical-trial adaptive-randomized-clinical-trial multicenter-randomized-clinical-trial cohort-study


Phase IV study

Phase IV Post-Marketing Surveillance Study

post-marketing surveillance study
post-approval study
Phase 4 study
PMS study

Formalised 1970s–1990s (ICH E3 guideline 1994)

Regulatory agencies and pharmaceutical industry (ICH, FDA, EMA frameworks)

A Phase IV study is a post-marketing surveillance study conducted after a drug, device, or intervention has received regulatory approval. Its primary purpose is to monitor long-term safety, detect rare adverse events, assess effectiveness in routine clinical practice, and explore new indications or populations not adequately represented in earlier trials. Phase IV evidence accumulates continuously throughout a product's commercial life.

Post-marketing observational or interventional study

Patient records, registries, adverse-event reports, survey data

International Conference on Harmonisation (ICH). (1994). ICH Harmonised Tripartite Guideline: Structure and Content of Clinical Study Reports E3. ICH Secretariat.

phase-iii-clinical-trial cohort-study case-control-study pharmacovigilance randomized-clinical-trial cross-sectional-epidemiological-study


Phase-Locked Loop

Phase-Locked Loop for Frequency Synchronization and Clock Recovery

A Phase-Locked Loop (PLL) is a feedback control system that synchronizes an output oscillator to match the phase and frequency of an input signal. Introduced by Gardner in 1966, PLLs are ubiquitous in communications, radar, clock distribution, and power systems. The PLL continuously adjusts its oscillator frequency to minimize the phase error with the input, achieving lock. PLLs are fundamental to modern electronic systems.

Feedback control loop for frequency and phase synchronization

Gardner, F. M. (1966). Phaselock Techniques. Wiley & Sons.

Wolaver, D. H. (1991). Phase-Locked-Loop Circuit Design. Prentice Hall.

Best, R. E. (2007). Phase-Locked Loops: Design, Simulation, and Applications (5th ed.). McGraw-Hill.


Phase-Locking Value

PLV
phase synchronization
phase coupling

Phase-Locking Value (PLV) is a frequency-domain measure of neural synchronization that quantifies the stability of phase difference between two signals. Introduced by Lachaux and colleagues in 1999, PLV detects phase coupling between brain regions independent of signal amplitude, enabling researchers to characterize functional connectivity from EEG and MEG recordings.

EEG/MEG functional connectivity analysis

Lachaux, J. P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208.

Varela, F., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229–239.


Phenological Observation

Crop Phenological Staging and Development Monitoring

Growth stage assessment
Phenological monitoring
Crop stage scale

Zadoks, Chang, Konzak (cereals); Fehr, Caviness (soybean)

Phenological Observation is an observational and classification pipeline for systematically recording crop development stages from germination to maturity. Standardized through crop-specific scales (Zadoks for cereals, Fehr for soybean), this method enables precise communication of crop status, timing of management decisions (fungicide application, irrigation), and prediction of harvest readiness.

Observational and classification pipeline

Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed research, 14(6), 415-421.

Fehr, W. R., & Caviness, C. E. (1971). Stages of soybean development. Iowa State University Special Report No. 80.

crop-growth-simulation crop-yield-estimation irrigation-scheduling-etref nitrogen-use-efficiency pesticide-efficacy-trial


Phenological Stage Monitoring

Temporal Tracking of Development Stages from Dormancy to Harvest

growth stage assessment
development monitoring
BBCH scale

Phenological stage monitoring uses standardized growth scales to track the developmental progression of plants from dormancy through flowering, fruit development, and maturity. The BBCH scale, formalized in 1997, provides a universal coding system for precise communication of developmental timing. This method enables optimization of management practices (pruning, fertilizing, spraying) at physiologically optimal moments and prediction of harvest timing.

Meier, U. (Ed.). (1997). Growth Stages of Mono- and Dicotyledonous Plants. BBCH Monograph (2nd ed.). Federal Biological Research Centre for Agriculture and Forestry.

Schwartz, M. D. (Ed.). (1999). Phenology: An Integrative Environmental Science. Kluwer Academic Publishers.

pruning-response-analysis crop-load-management pollination-efficiency postharvest-storage-simulation


Phenomenological Research

Phenomenology
Descriptive Phenomenology
Interpretive Phenomenology

Edmund Husserl (descriptive) and Martin Heidegger (interpretive)

Phenomenological research is a qualitative methodology focused on understanding the lived experience of a phenomenon as it is experienced by individuals. Rooted in the philosophical traditions of Edmund Husserl (descriptive phenomenology) and Martin Heidegger (interpretive phenomenology), this approach seeks to uncover the essential structures and meanings of human experience.

Husserl, E. (1931). Cartesian meditations: An introduction to phenomenology (D. Cairns, Trans.). Martinus Nijhoff.

Heidegger, M. (1962). Being and time (J. Macquarrie & E. Robinson, Trans.). Harper & Row.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. The State University of New York Press.


Phenomenology in education research

educational phenomenology
phenomenology of education
lived-experience research in education
pedagogical phenomenology

1990 (van Manen's systematic educational application); philosophical roots ~1900–1913

Max van Manen (education application); Edmund Husserl (philosophical foundation)

Phenomenology in education research is a qualitative approach that investigates how students, teachers, and educational actors experience pedagogical phenomena — learning, teaching, assessment, transition, or identity — from the inside. Drawing on van Manen's human science framework and Husserlian and Heideggerian traditions, it seeks to reveal the essential lived structures of educational experience rather than measure outcomes or test hypotheses.

In-depth interviews, reflective writing, classroom observations, student journals (text data)

van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.

Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (3rd ed.). Sage.

phenomenology hermeneutic-phenomenology interpretive-phenomenological-analysis narrative-inquiry case-study ethnography


Phenomenology

Fenomenoloji
phenomenological inquiry
phenomenological analysis

Early 20th century (Husserl ~1900–1913; Heidegger ~1927)

Edmund Husserl (transcendental); Martin Heidegger (hermeneutic)

Phenomenology is a qualitative research approach that investigates how participants live through and make sense of a specific experience. Rooted in the philosophy of Edmund Husserl and extended by Martin Heidegger, it aims to reveal the essential structures of lived experience rather than to measure or predict outcomes. The two most widely applied variants are Husserl's transcendental phenomenology, which seeks universal essences, and Heidegger's hermeneutic phenomenology, which emphasises interpretation within context.

In-depth interviews, focus groups (text data)

Transcendental (Husserlian) / Hermeneutic (Heideggerian)

Moustakas, C. (1994). Phenomenological Research Methods. Sage.

case-study narrative-analysis discourse-analysis grounded-theory ethnography thematic-analysis


PHF-COPRAS

PHF-COPRAS (Probabilistic Hesitant extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Song, H. F. Chen, Z. C. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Probabilistic Hesitant ranking — Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)

Song, H. F., Chen, Z. C. (2021). Multi-attribute decision-making method based distance and COPRAS method with probabilistic hesitant fuzzy environment. International Journal of Computational Intelligence Systems

ahp anp bwm critic entropy merec swara fucom


PHF-EDAS

PHF-EDAS (Extended Hesitant Fuzzy Linguistic EDAS (EHFL-EDAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Feng, X., Wei, C., Liu, Q. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Probabilistic Hesitant outranking/ranking — Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)

Feng, X., Wei, C., Liu, Q. (2018). EDAS Method for Extended Hesitant Fuzzy Linguistic Multi-criteria Decision Making. International Journal of Fuzzy Systems


PHF-TOPSIS

PHF-TOPSIS (Probabilistic Hesitant extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by PENDING_LITERATURE_SEARCH. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

PENDING_LITERATURE_SEARCH (). PENDING — PHF-TOPSIS specific seminal not confirmed. Zhang et al. 2017 (doi:10.1016/j.inffus.2017.02.001) is the foundational PHFS paper, not a PHF-TOPSIS paper. L.formulation.en cites 'Zhu & Xu 2018' as PHF-TOPSIS anchor — unverified. Candidate from search: Naeem et al. 2021 'Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information' (JIFS, doi:10.3233/JIFS-202700) — not confirmed as the canonical seminal..


PHF-VIKOR

PHF-VIKOR (Probabilistic Hesitant extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhang, S. Xu, Z. S. He, Y. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Zhang, S., Xu, Z. S., He, Y. (2017). Operations and integrations of probabilistic hesitant fuzzy information in decision making. Information Fusion


PHFS-EHVAR

PHFS-EHVAR (PHFS-EHVaR — Expected Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Extended tail decision-making method for probabilistic hesitant fuzzy environments. EHVaR improves upon HVaR by computing the expected (weighted sum) value over the entire left tail, not just the boundary point. EHVaR(h, X) = Σ_{i=1}^{k-1} c_i·p_i + c_k·(X - Σ_{i=1}^{k-1} p_i) where k satisfies P_{k-1} < X ≤ P_k. Always strictly separates PHFEs that HVaR cannot distinguish. Supports group decision-making via dynamic weight programming model.

Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing


PHFS-HVAR

PHFS-HVAR (PHFS-HVaR — Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Tail decision-making method for probabilistic hesitant fuzzy environments. Input is a PHFE (probabilistic hesitant fuzzy element) — an HFE where each membership value c_l carries an explicit occurrence probability p_l with Σp_l=1. HVaR(h, X) is the boundary membership value at cumulative probability X: the largest c_k such that P(c ≤ c_k) ≥ X. Directly analogous to classical Value-at-Risk (VaR). Intended for risk-averse investors who focus on worst-case outcomes under a given certainty degree.


Philadelphia Mindfulness Scale

Lizabeth A. Cardaciotto, James D. Herbert, and colleagues at Drexel University

The Philadelphia Mindfulness Scale (PHLMS) is a 20-item self-report instrument measuring trait mindfulness across two core dimensions: Present-Moment Awareness and Acceptance. Developed by Cardaciotto, Herbert, and colleagues at Drexel University and published in Assessment in 2008, the PHLMS emphasizes the integration of attentional and acceptance-based processes central to contemporary mindfulness theory and practice. The two-factor structure reflects the distinction between the ability to focus attention on present experience and the capacity to receive that experience without judgment or resistance—processes that jointly characterize psychological flexibility and adaptive mindfulness.

Cardaciotto, L., Herbert, J. D., Forman, E. M., Moitra, E., & Farrow, V. (2008). The assessment of present-moment awareness and acceptance: The Philadelphia Mindfulness Scale. Assessment, 15(2), 204-223.


Phillips-Ouliaris Test

Phillips-Ouliaris Residual-Based Cointegration Test

Phillips-Ouliaris Cointegration Test
PO Residual-Based Test
Residual-Based Cointegration Test
Phillips-Ouliaris Eşbütünleşme Testi

The Phillips-Ouliaris test, introduced by Phillips and Ouliaris in their 1990 Econometrica article, is a residual-based nonparametric procedure for testing the null hypothesis of no cointegration among a set of integrated I(1) time series. It corrects OLS residuals from a cointegrating regression for serial correlation and endogeneity using kernel-based long-run variance estimators, yielding two statistics—Z_alpha (variance-ratio) and Z_t (normalized coefficient)—whose asymptotic distributions are tabulated specifically for systems with multiple stochastic regressors.

Residual-based nonparametric cointegration test

Z_alpha (variance-ratio) and Z_t (unit-root)

Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica, 58(1), 165–193.


Phillips-Perron Test

PP test
Phillips-Perron unit root test
Phillips-Perron birim kök testi

The Phillips-Perron test, proposed by Peter Phillips and Pierre Perron in 1988, tests for a unit root in a time series, like the Augmented Dickey-Fuller test, but corrects for autocorrelation and heteroskedasticity in the errors non-parametrically rather than by adding lagged differences. It runs a simple Dickey-Fuller regression and then adjusts the test statistic using a long-run variance estimate, so the practitioner need not choose a lag length for the regression itself.

Series contains a unit root (non-stationary)

Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.

Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.

adf-test kpss-test cointegration-test arima


Phillips-Perron unit root test

PP test
PP unit root test
Phillips-Perron test
nonparametric unit root test

The Phillips-Perron (PP) test is a nonparametric unit root test for time series that corrects for serial correlation and heteroscedasticity in the error term without adding lagged differences. Introduced by Phillips and Perron (1988), it applies a kernel-based long-run variance estimator to adjust the Dickey-Fuller statistic, making it robust to a wide class of weakly dependent error processes.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

augmented-dickey-fuller-unit-root-test kpss-test zivot-andrews-structural-break-test granger-causality-test johansen-cointegration-test arima-model


Brief Phobia Scales

Brief Phobia Scales for Specific Phobias

Steven R. Woody, Jarl M. Lohr, and multiple developers of specific phobia scales

Brief Phobia Scales are a collection of short, focused self-report instruments designed to measure fear and anxiety related to specific phobias such as agoraphobia, claustrophobia, fear of flying, fear of heights, and other circumscribed fears. Developed by various researchers including Woody and Lohr, these scales provide rapid, phobia-specific assessment in clinical and research settings, valued for their brevity and practical utility in treatment planning and outcome monitoring.

Woody, S. R., & Lohr, J. M. (2005). A review of brief instruments for assessing anxiety and phobia. In C. Newman, C. Z. Datillio, & R. P. Halstead (Eds.), Comprehensive Handbook of Cognitive and Behavioral Treatments for Anxiety Disorders. New York: Routledge.


Photogrammetry

3D photogrammetry
structure from motion photogrammetry
SfM photogrammetry
digital photogrammetry

1850s (foundations); 2000s (digital/SfM era in life sciences)

Multiple contributors (Laussedat ~1850s; modern SfM by Longuet-Higgins 1981)

Photogrammetry is a non-contact measurement technique that derives accurate 3D geometry and spatial dimensions from sets of overlapping 2D photographs. In veterinary science it is used to obtain body measurements, wound areas, limb morphology, and anatomical volumes from live animals, carcasses, or skeletal specimens without physical restraint or invasive procedures. Structure-from-Motion (SfM) algorithms have made the method accessible to field and clinic settings using consumer-grade cameras.

Overlapping 2D photographs (digital images)

Falkingham, P. L. (2012). Acquisition of high resolution three-dimensional models using free, open-source, photogrammetric software. Palaeontologia Electronica, 15(1), 1T.

Sherrill, L. R., et al. (2019). Use of photogrammetry to measure body dimensions and predict body weight of beef cattle. Translational Animal Science, 3(3), 1467-1478.

computed-tomography morphometric-analysis image-analysis 3d-scanning digital-image-correlation ultrasound-imaging


Photoplethysmography

PPG
Pulse oximetry
Reflectance photometry

Photoplethysmography (PPG) measures blood volume changes in tissue using light absorption, providing a non-invasive optical window into cardiovascular dynamics. Originally developed by Hertzman in 1937, PPG is now ubiquitous in pulse oximetry, smartwatches, and research applications for monitoring heart rate, blood oxygenation, and vascular function.

Optical signal acquisition and analysis pipeline

Allen, J. (2007). Photoplethysmography and its application in clinical physiology. Physiology & Behavior, 107(4), 540-548.

Webster, J. G. (2002). Medical Instrumentation: Application and Design (4th ed.). Wiley.


PHQ-9 Depression Screening

Kurt Kroenke, Robert L. Spitzer, Janet B. W. Williams

The Patient Health Questionnaire-9 (PHQ-9) is a brief, 9-item self-report instrument for screening and measuring the severity of depressive symptoms in primary care and mental health settings. Developed by Kurt Kroenke and colleagues in 2001, the PHQ-9 is now widely used in healthcare systems worldwide as a rapid, accurate screening and monitoring tool.

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613.

Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1–3), 163–173.


Patient Health Questionnaire-9

Patient Health Questionnaire-9: Depression Scale

Patient Health Questionnaire Depression Module

The PHQ-9 is a brief, nine-item self-report questionnaire developed by Kroenke, Spitzer, and Williams to screen for and measure the severity of depressive symptoms. Published in 2001 in the Journal of General Internal Medicine, it has become one of the most widely used depression screening instruments globally. The scale maps directly to DSM-IV diagnostic criteria for major depressive disorder, making it valuable in both clinical and research settings.

Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613.

Kroenke, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. General Hospital Psychiatry, 32(4), 345–359.

Manea, L., Gilbody, S., & McMillan, D. (2012). Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ, 184(15), E682–E689.

bdi-ii hamilton-depression-rating-scale montgomery-asberg-depression quick-inventory-depressive patient-global-impression-change


Phylogenetic Analysis

Phylogenetic Analysis of Molecular Sequence Data

molecular phylogenetics
phylogenetic inference
evolutionary tree reconstruction
phylogenomics

1960s-1981 (distance trees ~1967; ML framework formalised 1981)

Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)

Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone technique in evolutionary biology, microbiology, epidemiology, and comparative genomics, supporting tasks from tracing viral outbreak origins to classifying novel species.

Aligned nucleotide or amino acid sequences (FASTA/NEXUS), distance matrices

Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates.

Felsenstein, J. (1981). Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution, 17(6), 368-376.

sequence-alignment genome-wide-association-study microbiome-diversity-analysis rna-seq-differential-expression variant-calling eqtl-analysis


Phylogenetic Independent Contrasts

Phylogenetic Independent Contrasts for Comparative Analysis

PIC
Contrasts method
Felsenstein's contrasts

Phylogenetic Independent Contrasts (PIC) is a comparative statistical method that tests for associations between traits across species while accounting for shared evolutionary history. Developed by Joseph Felsenstein in 1985, PIC solves a fundamental problem in comparative biology: related species share traits due to common ancestry, not independent evolution, which violates the statistical assumption of independence. By comparing trait differences between sister species pairs, PIC removes the confounding effects of phylogenetic relatedness and enables robust evolutionary inferences.

Felsenstein, J. (1985). Phylogenies and the comparative method. American Naturalist, 125(1), 1–15.

Harvey, P. H., & Pagel, M. D. (1991). The comparative method in evolutionary biology. Oxford: Oxford University Press.

Garland, T., Harvey, P. H., & Ives, A. R. (1992). Procedures for the analysis of comparative data using phylogenetically independent contrasts. Systematic Biology, 41(1), 18–32.


Physical Activity Enjoyment Scale

Dorothy Kendzierski and Kenneth J. DeCarlo

The Physical Activity Enjoyment Scale (PACES), developed by Kendzierski and DeCarlo (1991), is a 16-item measure of the positive affective responses and enjoyment experienced during or after physical activity. Based on the premise that enjoyment is a powerful predictor of exercise adherence and intrinsic motivation, PACES assesses feelings such as pleasure, fun, satisfaction, and interest during physical activity. The instrument uses semantic differential responses (e.g., 'boring–interesting', 'dull–fun', 'unpleasant–pleasant') to capture the hedonic experience of exercise. PACES is widely used in exercise science, health promotion, and physical education research to identify activities that are most enjoyable for specific populations and to evaluate whether interventions enhance exercise enjoyment.

Kendzierski, D., & DeCarlo, K. J. (1991). Physical Activity Enjoyment Scale: two validation studies. Journal of Sport and Exercise Psychology, 13(1), 50-64.


Physical Self-Description Questionnaire

Physical Self-Description Questionnaire (PSDQ)

The PSDQ is a 40-item questionnaire measuring multidimensional physical self-concept—how individuals perceive and evaluate themselves across 11 physical domains including strength, endurance, body appearance, sports competence, and fitness. Developed by Marsh and colleagues in 1994, the PSDQ has become the leading instrument for assessing physical self-concept in youth and adult athletes and is valuable for understanding body image, exercise motivation, and psychological wellbeing.

Self-report multidimensional physical self-concept questionnaire

Marsh, H. W., Richards, G. E., Johnson, S., Roche, L., & Tremayne, P. (1994). Physical Self-Description Questionnaire: Psychometric properties and a multitrait-multimethod analysis. Journal of Sport & Exercise Psychology, 16(3), 270–305.

Marsh, H. W., Papaioannou, A., & Theodorakis, Y. (2006). Motivational constructs across cultures: Validation of expectancy-value theory and dimensional comparison model. Journal of Educational Psychology, 98(2), 396–409.

sport-confidence-inventory athletic-identity-measurement-scale mental-toughness-questionnaire task-ego-orientation-sport


Physiologically Based Pharmacokinetics

Physiologically Based Pharmacokinetics (PBPK)

PBPK is a mechanistic modeling framework that uses physiological parameters, tissue properties, and drug-specific attributes to predict drug concentration time profiles in the body. Developed rigorously in the 1990s by researchers including Nestorov, PBPK integrates anatomy, biochemistry, and kinetics to enable rational drug development, bridging in vitro data to clinical outcomes.

Nestorov, I. (1997). Sensitivity analysis of pharmacokinetic and pharmacodynamic systems. Journal of Pharmacokinetics and Biopharmaceutics, 25(4), 529-543.

Reddy, M. B., Yang, R. S., Clewell, H. J., & Andersen, M. E. (2005). Physiologically based pharmacokinetic modeling: science and applications. Hoboken, NJ: John Wiley & Sons.


Phytolith Analysis

Phytolith Analysis (Plant Silica Body Analysis)

plant opal analysis
opal phytolith analysis
phytolith morphotype analysis

Archaeobotany / Paleoecology / Soil Science

1841 (first description); modern analytical framework 1970s–1990s

Multiple contributors (Ehrenberg, 1841; systematised by Rovner and Piperno, late 20th century)

Phytolith analysis is a laboratory technique used to identify and quantify microscopic silica bodies deposited in plant cells, recovered from soils, sediments, or archaeological contexts. Because phytoliths preserve long after organic material has decayed, the method is central to reconstructing past vegetation, crop histories, land use, and soil development across agronomy, paleoecology, and archaeobotany.

Microscopic silica bodies extracted from plant tissue or sediment

Piperno, D. R. (2006). Phytoliths: A Comprehensive Guide for Archaeologists and Paleoecologists. AltaMira Press.

Strömberg, C. A. E. (2011). Evolution of grasses and grassland ecosystems. Annual Review of Earth and Planetary Sciences, 39, 517–544.

pollen-analysis stable-isotope-analysis sediment-micromorphology dendrochronology soil-organic-matter-analysis remote-sensing-vegetation


Phytoplankton Size Class

Size-fractionated Chlorophyll
Phytoplankton Taxonomy

Phytoplankton size classification is a fundamental framework for organizing plankton communities and understanding their ecological roles and biogeochemical impacts. Developed by Sieburth, Smetacek, and Lenz in 1978, size classes (pico-, nano-, micro-, macro-phytoplankton) define distinct functional groups with different nutritional requirements, growth rates, grazing vulnerabilities, and sinking rates. Size-based classification enables rapid assessment of plankton community structure and prediction of ecosystem responses to environmental change.

Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnology and Oceanography, 23(6), 1256-1263.

Malone, T. C. (1980). Algal size. In I. Morris (Ed.), The Physiological Ecology of Phytoplankton (pp. 433-463). University of California Press.


Picker Patient Experience Questionnaire

The Picker Patient Experience Questionnaire is a comprehensive, validated instrument developed by the Picker Institute to measure the quality of the patient experience across multiple dimensions of healthcare delivery. Administered post-discharge or post-encounter, it assesses ten domains of patient-centered care: respect and dignity, information and communication, emotional support, involvement in decisions, continuity of care, coordination, access, physical comfort, emotional needs, and overall experience. The Picker questionnaire has become a standard measure in health systems internationally for evaluating and improving patient experience and has been used extensively in both inpatient and outpatient settings.

Jenkinson, C., Coulter, A., Bruster, S., Richards, N., & Chandola, T. (2002). Patients' experiences and satisfaction with health care: results of a questionnaire study of specific aspects of care. British Medical Journal, 324(7329), 860-864.

Coulter, A., Jenkinson, C., Jenkinson, C. (2005). European patient satisfaction with health care and control over health. European Journal of Public Health, 15(3), 282-290.

collaboste-scale patient-reported-communication-scale patient-enablement-instrument care-transitions-measure


Picrosirius Red Staining

Picrosirius Red Collagen Visualization Assay

sirius red
collagen staining
fibrillar collagen assay

Picrosirius red (acid red 80) is a direct dye for collagen that binds specifically to the triple helix structure of fibrillar collagens and allows direct visualization and quantification under light and polarized light microscopy. Introduced by Junqueira and colleagues in 1978, picrosirius red staining has become the gold standard for assessing collagen deposition and organization in tissue sections, scaffolds, and cell cultures. The key advantage is that picrosirius red-stained collagen exhibits birefringence under polarized light, enabling researchers to visualize not only the amount of collagen but also its degree of organization and fibril maturity—information crucial for evaluating bone, cartilage, skin, and tendon engineering.

Junqueira, L. C. U., Bignolas, G., & Brentani, R. R. (1978). Picrosirius staining plus polarization microscopy, a specific method for collagen detection in tissue sections. Histochemical Journal, 11(4), 447-455.

Whittaker, P., Kloner, R. A., Boughner, D. R., & Pickering, J. G. (1994). Quantitative assessment of myocardial collagen with picrosirius red staining and circularly polarized light microscopy. Basic Research in Cardiology, 89(6), 476-484.

Bickel, M., Touzel, R., & Buisson, A. C. (2011). Isolation and characterization of collagen types from fish skin. Comparative Biochemistry and Physiology, 160(2), 147-154.

alizarin-red-staining dynamic-mechanical-analysis swelling-and-degradation electrospinning


PIF-ARAS

PIF-ARAS (PiF-ARAS — Picture Fuzzy extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture utility-degree ranking — Picture Fuzzy Number (PiFN: μ, η, ν; μ+η+ν ≤ 1)

Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


PIF-ARTASI

PIF-ARTASI (Picture Fuzzy ARTASI (Kara et al. 2024)) is a ranking multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture outranking/ranking — PiFS (μ, η, ν; μ+η+ν ≤ 1) + adaptive standardized intervals

Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing


PIF-CIMAS-ARTASI

PIF-CIMAS-ARTASI (Hybrid Picture Fuzzy weighting (CIMAS) + ranking (ARTASI)) is a hybrid multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Two-stage MAGDM: PIF-CIMAS criterion weights → PIF-ARTASI alternative ranking


PIF-CIMAS

PIF-CIMAS (Picture Fuzzy variant of CIMAS (Kara et al. 2024)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture Fuzzy criteria weighting via expert assessment (CIMAS) — PFWA aggregation

pif-artasi pif-cimas-artasi pif-codas pif-copras pif-edas pif-marcos pif-moora pif-saw


PIF-CODAS

PIF-CODAS (PiF-CODAS — Picture extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture distance-based ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)


PIF-COPRAS

PIF-COPRAS (PiF-COPRAS — Picture extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)

Cuong, B. C. (2013). Picture fuzzy sets — first results, parts I and II. Seminar on Neuro-Fuzzy Systems with Applications, Institute of Mathematics, Hanoi (preprint); reprinted as Cuong (2014), J. Comput. Sci. Cybernet. 30, 409–420


PIF-DOMBI

PIF-DOMBI (Picture Fuzzy Dombi Aggregation Operators for MADM (Jana, Senapati, Pal & Yager 2019)) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Jana, C. Senapati, T. Pal, M. Yager, R. R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Dombi t-norm/t-conorm aggregation on Picture Fuzzy Numbers (PFN: μ,η,ν; μ+η+ν≤1). PFDWA (arithmetic, Def.13/Eq.2) primary, PFDWG (geometric, Def.16/Eq.7) companion. Ranking by score Ê(T)=(1+μ-ν)/2 (Def.6) with accuracy L̂(T)=μ-ν tie-break (Def.7).

Jana, C., Senapati, T., Pal, M., Yager, R. R. (2019). Picture fuzzy Dombi aggregation operators: Application to MADM process. Applied Soft Computing


PIF-EDAS

PIF-EDAS (PiF-EDAS — Picture extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PIF-MARCOS

PIF-MARCOS (PiF-MARCOS — Picture extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PIF-MOORA

PIF-MOORA (PiF-MOORA — Picture extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PIF-SAW

PIF-SAW (PiF-SAW — Picture extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PIF-TODIM

PIF-TODIM (PiF-TODIM — Picture extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture outranking/ranking — Picture Fuzzy Number (PiFN: ⟨μ, η, ν⟩; μ+η+ν ≤ 1) with prospect-theory loss aversion


PIF-TOPSIS

PIF-TOPSIS (PiF-TOPSIS — Picture extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PIF-VIKOR

PIF-VIKOR (PiF-VIKOR — Picture extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


PIF-WASPAS

PIF-WASPAS (PiF-WASPAS — Picture Fuzzy extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Picture WSM-WPM hybrid ranking — Picture Fuzzy Number (PiFN: μ, η, ν; μ+η+ν ≤ 1)


Pilot AB Design

Pilot AB Single-Subject Experimental Design

pilot AB phase design
preliminary AB design
exploratory AB single-case design
feasibility AB design

1960s (AB design); pilot framing formalized in practice by 1980s–1990s

Murray Sidman; Baer, Wolf & Risley (AB logic); pilot application emergent from single-subject research practice

A pilot AB design applies the two-phase baseline-then-intervention structure of the AB single-subject design in an explicitly exploratory or feasibility mode — before committing to a more rigorous reversal or multiple-baseline study. The researcher collects repeated baseline (A) and intervention (B) data from one or a few individuals primarily to test measurement procedures, estimate effect size, verify data stability, and determine whether a stronger single-case design is warranted and feasible.

Single-subject pilot experimental design

Repeatedly measured behavioral or outcome data over time (individual participant)

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97.

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.

ab-design aba-design abab-design multiple-baseline-design single-subject-experimental-design pilot-randomized-controlled-trial


Pilot A/B Test

Pilot A/B Test (Preliminary Split-Test Experiment)

pilot split test
feasibility A/B test
preliminary A/B experiment
pilot randomized comparison

2000s–2010s (formalized in digital experimentation literature)

Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice

A Pilot A/B test is a small-scale, preliminary split-test experiment run before a full A/B test to assess feasibility, estimate effect sizes, detect operational problems, and validate measurement instruments. Participants are randomly assigned to a control condition (A) and a treatment condition (B), but the study is explicitly underpowered — its purpose is to inform the design of the definitive test, not to yield a conclusive comparison.

Continuous, binary, or ordinal outcome measurements from two randomly assigned groups

Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10(1), 1.

Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2009). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18(1), 140-181.

ab-test pilot-randomized-controlled-trial multi-arm-experiment adaptive-ab-test pragmatic-ab-test factorial-ab-test


Pilot ABA Design

Pilot ABA reversal design
Pilot withdrawal design
Pilot single-subject reversal design
Feasibility ABA design

1960s (ABA design); pilot adaptation in clinical and behavioral research from 1980s onward

Murray Sidman (ABA reversal logic); pilot study methodology broadly attributed to clinical trial traditions

The Pilot ABA Design is a small-scale single-subject experiment that applies the ABA reversal structure — baseline, intervention, withdrawal — to test the feasibility, acceptability, and preliminary effect of an intervention before committing to a full-scale study. It provides early evidence of whether the treatment produces a detectable change and whether the reversal is ethically and practically achievable.

Single-subject experimental pilot design

Repeated-measures behavioral or clinical outcome data on a single participant or small series

Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.

Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.

aba-design abab-design pilot-ab-design multiple-baseline-design single-subject-experimental-design pilot-randomized-controlled-trial


Pilot ABAB Design

pilot reversal design
feasibility ABAB design
pilot withdrawal design
pilot single-subject reversal

1960s (ABAB base); pilot application codified c. 2000s

Derived from ABAB reversal design (Sidman, 1960); pilot framing formalized in behavioral intervention feasibility literature (late 20th–early 21st century)

A Pilot ABAB design is a small-scale feasibility trial of the ABAB reversal design, conducted with one or a few participants to test whether an intervention produces reliable behavior change under alternating baseline and treatment conditions before committing resources to a larger study. It combines the internal-validity logic of the ABAB reversal with the limited scope and preliminary aims of a pilot investigation.

Single-subject experimental feasibility design

Repeated behavioral observations (continuous or interval recording) per individual participant

Byiers, B. J., Reichle, J., & Symons, F. J. (2012). Single-subject experimental design for evidence-based practice. American Journal of Speech-Language Pathology, 21(4), 397–414.

Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Allyn & Bacon.

abab-design aba-design ab-design multiple-baseline-design pilot-randomized-controlled-trial single-subject-experimental-design


Pilot Cluster Sampling

pilot area sampling
feasibility cluster sample
preliminary cluster survey
pilot cluster survey

Mid-20th century (cluster sampling foundations); 2000s (pilot study formalization)

Rooted in W. G. Cochran's cluster sampling theory (1953) combined with pilot-study methodology formalized by Lancaster, Dodd & Williamson (2004) and Thabane et al. (2010)

Pilot cluster sampling is the application of a cluster sampling protocol on a small, preliminary scale to evaluate the feasibility, logistics, and parameter estimates needed before committing to a full-scale cluster survey. A subset of clusters is randomly selected and fully surveyed, yielding estimates of the intraclass correlation (ICC), design effect, recruitment rates, and operational costs. These findings directly inform the sample size and cluster allocation of the definitive survey.

Quantitative or mixed; unit-level measurements within naturally occurring groups; recruitment and logistical feasibility metrics

Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.

cluster-sampling pilot-stratified-sampling pilot-systematic-sampling multistage-sampling adaptive-cluster-sampling proportional-cluster-sampling


Pilot Control Group Experimental Design

Pilot Study with Control Group Experimental Design

pilot controlled experiment
pilot RCT feasibility study
small-scale controlled trial
pilot control group study

Mid-20th century; widely formalized by 1980s–2000s

Established through clinical and behavioral research traditions; formalized by Bradford Hill and colleagues in mid-20th century trial methodology

A pilot control group experimental design is a small-scale, preliminary experiment that includes both a treatment group and a control group, conducted before the main study to test whether the full trial is feasible. It produces early effect-size estimates, identifies protocol problems, and confirms that random (or systematic) assignment to conditions is workable — all while generating a genuine comparison between treated and untreated participants.

Experimental design (pilot/feasibility variant)

Quantitative (continuous, ordinal, or count outcomes from intervention and control participants)

Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10, 1.

Campbell, M., Fitzpatrick, R., Haines, A., Kinmonth, A. L., Sandercock, P., Spiegelhalter, D., & Tyrer, P. (2000). Framework for design and evaluation of complex interventions to improve health. BMJ, 321(7262), 694–696.


Pilot Deviant Case Sampling

pilot extreme case sampling
pilot outlier sampling
pilot atypical case sampling
pilot deviant-case pilot sampling

2002 (Patton codification); pilot study tradition mid-20th century

Patton (deviant case sampling); pilot study methodology attributed broadly to pre-main-study practice traditions

Pilot deviant case sampling is a purposive sampling strategy applied during a pilot phase in which participants are deliberately selected because they represent extreme, unusual, or atypical instances of the phenomenon under study. Rather than seeking representative participants for the pilot run, the researcher intentionally recruits outlier cases to probe the boundaries of research instruments, interview guides, or data collection protocols before the main study begins.

Purposive sampling variant applied in pilot phase

Qualitative or mixed-methods data from interviews, observations, or instruments

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Sampling Strategies, pp. 230–246 on deviant/extreme case sampling]

van Teijlingen, E. R., & Hundley, V. (2002). The importance of pilot studies. Nursing Standard, 16(40), 33–36.

deviant-case-sampling pilot-purposive-sampling pilot-maximum-variation-sampling extreme-case-sampling purposive-sampling pilot-typical-case-sampling


Pilot Factorial Experiment

preliminary factorial study
pilot factorial design
small-scale factorial trial
feasibility factorial experiment

1930s (Fisher); pilot application conventions developed mid-20th century

R. A. Fisher (factorial design foundations); formalized in experimental statistics literature

A pilot factorial experiment is a small-scale, preliminary study that employs a factorial structure to simultaneously vary two or more factors across a limited number of experimental units. Its purpose is not to deliver definitive conclusions but to estimate effect sizes, within-group variance, and factor interactions, and to test logistical feasibility before committing resources to a full-scale factorial experiment. It is widely used in behavioral sciences, engineering, agriculture, and clinical research as an essential planning step.

Continuous or categorical outcome measures; multiple independent factors

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.

full-factorial-design fractional-factorial-design two-level-factorial-design response-surface-methodology randomized-controlled-trial pilot-study


Pilot Field Experiment

pilot field trial
small-scale field experiment
feasibility field experiment
exploratory field experiment

Mid-20th century (systematised 1960s–1990s)

Rooted in Campbell & Stanley (1966) experimental design tradition; formalised in clinical and social research through the 20th century

A pilot field experiment is a small-scale, preliminary version of a planned full field experiment conducted in a naturalistic setting. It tests whether the intervention, randomisation procedure, measurement instruments, and logistical protocols are feasible before committing to a full-scale study. Results inform sample size calculations, refine treatment protocols, and identify procedural risks — saving resources and improving the quality of the definitive study.

Quantitative (outcome measures, effect sizes, attrition rates); mixed methods possible

Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Rand McNally.

Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of Psychiatric Research, 45(5), 626–629.

randomized-controlled-trial quasi-experiment field-experiment feasibility-study pretest-posttest-design cluster-randomized-trial


Pilot Fractional Factorial Experiment

pilot FFE
screening pilot design
pilot fractional factorial
pilot FF screening study

1950s–1960s (fractional factorial foundation); pilot study integration formalized in 20th century DOE practice

Box, Hunter & Hunter (fractional factorial); pilot study concept developed broadly in industrial and clinical experimentation

A pilot fractional factorial experiment is a small-scale preliminary study that uses a fractional factorial design — testing only a subset of all possible factor combinations — to screen multiple factors simultaneously before committing to a full-scale investigation. It provides early estimates of effect sizes, variance, and feasibility at substantially reduced cost and participant burden compared to a full factorial pilot or a full-scale trial.

Experimental screening design (pilot phase)

Continuous or categorical outcome measurements from a small experimental run

fractional-factorial-experiment full-factorial-experiment pilot-randomized-controlled-trial factorial-experiment response-surface-methodology plackett-burman-design


Pilot full factorial experiment

pilot factorial design
pilot 2^k design
pilot complete factorial experiment
screening factorial pilot

1920s (Fisher); pilot usage formalised mid-20th century

R. A. Fisher (full factorial foundations); pilot application codified in applied DOE literature (Box, Hunter & Hunter; Montgomery)

A pilot full factorial experiment is a small-scale, complete crossing of all selected factors at all their levels, run before a definitive study to gather preliminary effect estimates, assess variability, and verify experimental logistics. It retains the complete combinatorial structure of a full factorial design — every combination of factor levels is tested — but is intentionally limited in scope (fewer replicates, narrower factor ranges) to conserve resources while maximising learning about factor effects and interactions before committing to a larger investigation.

Experimental design (pilot/screening phase)

Continuous or categorical outcome measurements from a controlled experiment

full-factorial-experiment fractional-factorial-experiment two-level-factorial-design response-surface-methodology one-factor-at-a-time randomized-complete-block-design


Pilot Multi-Arm Experiment

pilot multi-arm trial
feasibility multi-arm study
pilot parallel-arm experiment
preliminary multi-arm experiment

Evolved from clinical trial methodology; consolidated in the 1990s–2000s

A pilot multi-arm experiment is a small-scale preliminary trial that tests the feasibility, logistics, and parameter estimates needed to plan a full-scale multi-arm study. It simultaneously evaluates two or more active treatment arms alongside a control, providing early evidence on recruitment rates, retention, protocol adherence, variability, and likely effect sizes before committing to a resource-intensive definitive experiment.

Continuous, categorical, or binary outcome measures; feasibility indicators

Chow, S.-C., & Liu, J.-P. (2008). Design and Analysis of Clinical Trials: Concepts and Methodologies (2nd ed.). Wiley-Interscience.

multi-arm-experiment pilot-randomized-controlled-trial adaptive-multi-arm-experiment factorial-experiment pilot-field-experiment randomized-controlled-trial


Pilot Multistage Sampling

pilot MSS
multistage pilot sampling
trial multistage sampling
multistage sampling pilot test

Survey methodology tradition; formalized in Kish (1965) and later survey practice literature

Pilot multistage sampling applies a small-scale trial run of a multistage sampling design before committing to the full fieldwork. The researcher draws a mini-version of the hierarchical sample — typically spanning the same stages (e.g., regions, then clusters, then individuals) — to test frame quality, stage-transition procedures, and variance estimates, then uses those findings to calibrate the main sampling plan.

Probability sampling with pilot validation phase

Survey data (quantitative or mixed); frame documents, cluster lists

Kish, L. (1965). Survey Sampling. John Wiley & Sons.

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). John Wiley & Sons.

multistage-sampling stratified-sampling cluster-sampling systematic-sampling pilot-stratified-sampling pilot-cluster-sampling