PIPP

The PIPP is a seven-indicator behavioral and physiological pain assessment tool specifically designed for preterm and full-term infants undergoing painful procedures. Developed by Stevens et al. in 1996, it measures acute procedural pain by integrating gestational age, behavioral state, facial expressions, and vital sign changes. The PIPP has become the most widely validated neonatal acute pain instrument in research and is recommended by major pediatric pain organizations for assessing pain during routine NICU procedures.

Stevens, B., Johnston, C., Petryshen, P., & Taddio, A. (1996). Premature Infant Pain Profile: Development and Initial Validation. Clinical Journal of Pain, 12(1), 13-22.

Stevens, B. J., Gibbins, S., Yamada, J., et al. (2014). Epidemiology and Management of Painful Procedures in Infants in Canadian Neonatal Intensive Care Units. Canadian Medical Association Journal, 186(6), E225-E234.


Pretest-Posttest Experimental Design

pretest-posttest design
before-after design
pre-post design
two-wave experimental design

Donald T. Campbell and Julian C. Stanley

The pretest-posttest experimental design measures participants on the outcome variable before and after treatment, typically with random assignment to treatment and control groups. The difference between pre- and post-scores isolates the treatment effect from baseline variation, making this one of the most widely used frameworks in experimental and quasi-experimental research across education, psychology, medicine, and the social sciences.

Experimental / quasi-experimental research design

Quantitative outcome measures (continuous, ordinal, or count data) collected at two time points

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

randomized-controlled-trial solomon-four-group-design control-group-experimental-design factorial-experiment repeated-measures-design difference-in-differences


Price Fairness Scale

Pricing Justice Scale
Fair Price Perception Scale

Ling Xia, Kent B. Monroe, Jennifer L. Cox

The Price Fairness Scale (PFS), developed by Xia, Monroe, and Cox (2004), measures customer perception of whether a charged price is fair and reasonable relative to value received and market comparison. Price fairness assessment differs from absolute price satisfaction: customers may perceive a price as high but fair if quality justifies it, or as low but unfair if they suspect price discrimination or exploitation. The PFS captures three dimensions of price fairness judgment: Distributive Fairness (whether the price-value ratio is equitable), Procedural Fairness (whether the pricing process is transparent and non-discriminatory), and Interactional Fairness (whether pricing explanations are respectful). The scale is critical for premium pricing strategy, price increases, and dynamic pricing implementation.

Campbell, M. C. (2005). Perceived Price Fairness. MIT Sloan Management Review, 46(3), 30-35.

Xia, L., Monroe, K. B., & Cox, J. L. (2004). The Price is Unfair! A Conceptual Framework and Research Agenda on Perceived Price Fairness. Journal of Marketing, 68(4), 1-15.


Van Westendorp Price Sensitivity Meter

Van Westendorp Price Sensitivity Meter Framework

Price Sensitivity Meter
PSM
Van Westendorp Method

The Van Westendorp Price Sensitivity Meter is a market research method developed by Peter van Westendorp in 1993 for assessing consumer price perception and estimating willingness-to-pay ranges without directly asking customers their maximum price. The method uses four simple questions about price acceptability, yielding estimates of optimal price, acceptable price range, and price perception zones.

Van Westendorp, P. (1993). Price Perception Analysis. An Application to the International Car Market. International Journal of Research in Marketing, 10(2), 157-165.

Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How Should Consumers' Willingness to Pay Be Measured? A Managerial Perspective. Journal of Product & Brand Management, 20(6), 460-469.

Chernev, A., & Hamilton, R. (2009). Assortment Size and Option Attractiveness in Consumer Choice Among Retailers. Journal of Marketing Research, 46(3), 410-420.

willingness-to-pay-estimation market-segmentation-analysis marketing-mix-modeling brand-equity-measurement customer-lifetime-value


Primary Care PTSD Screen for DSM-5

The PC-PTSD-5 is a 5-item self-report screening instrument for posttraumatic stress disorder (PTSD) aligned with DSM-5 diagnostic criteria. Developed by Prins and colleagues in 2015 as an update to the earlier 4-item PC-PTSD, the PC-PTSD-5 is designed specifically for rapid screening in primary care and other non-specialist medical settings. It is freely available, brief, and demonstrates strong sensitivity and specificity for identifying individuals warranting full PTSD diagnostic evaluation.

Prins, A., Bovin, M. J., Smolenski, D. J., et al. (2015). The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5): Development and evaluation within a veteran primary care sample. Journal of General Internal Medicine, 31(10), 1206-1211.

Prins, A., Smolenski, D. J., Bovin, M. J., et al. (2016). Psychometric evaluation of the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5). Depression and Anxiety, 33(S1), 91.


Principal-Agent Model

Principal-Agent Model with Asymmetric Information and Moral Hazard

Agency Theory
Hidden Action Problem
Moral Hazard

Michael Jensen, William Meckling, Bengt Holmstrom

The Principal-Agent Model analyzes how a principal (e.g., owner, employer, policymaker) can incentivize an agent (e.g., manager, employee, firm) to act in the principal's interest when the agent has private information or can take hidden actions. Formalized by Jensen and Meckling in 1976, the model identifies agency costs arising from moral hazard (the agent exerts less effort than desired) and adverse selection (the agent hides unfavorable information). Optimal contracts balance incentives with risk allocation.

Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.

Holmstrom, B. (1991). The firm as a subpoena server. Journal of Law, Economics and Organization, 7(2), 53-64.

bayesian-nash-equilibrium vcg-mechanism shapley-value nash-equilibrium


Principal Component Risk Factors

Risk Factor PCA via Return Covariance Decomposition

Risk Faktörü PCA (Getiri Kovaryans Ayrışımı)

Litterman & Scheinkman (bond-return factors); Connor & Korajczyk (statistical APT factors)

Risk Factor PCA is a dimension-reduction method that decomposes the return covariance matrix of many assets into a small set of orthogonal principal components interpreted as systematic risk factors. Litterman and Scheinkman (1991) used it to show that bond returns are driven by a few common factors, and Connor and Korajczyk (1988) developed the statistical-factor interpretation for the APT.

Statistical factor model (dimension reduction)

Eigendecomposition of the return covariance (or correlation) matrix

Litterman, R. & Scheinkman, J. (1991). Common Factors Affecting Bond Returns. Journal of Fixed Income, 1(1), 54-61.

Connor, G. & Korajczyk, R. A. (1988). Risk and Return in an Equilibrium APT: Application of a New Test Methodology. Journal of Financial Economics, 21(2), 255-289.

portfolio-optimization-mean-variance interest-rate-models credit-risk-models factor-analysis ols-regression


Principal Components Regression

PCR
PCA regression
temel bileşenler regresyonu

Principal-component regression literature (Jolliffe and others)

Principal components regression first compresses a set of correlated predictors into a few principal components — the directions of greatest variance — and then regresses the response on those components. By discarding low-variance directions, PCR stabilizes estimation in the presence of multicollinearity and high dimensionality, at the cost of choosing components without reference to the response.

Unsupervised dimension reduction + regression

Top principal components of the predictors

Output: Regression on a few PCs, mapped back to predictors

Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300–303.

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

partial-least-squares principal-component-analysis ridge-regression multiple-linear-regression


PRISMA-based review

Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based Review

PRISMA review
PRISMA-guided systematic review
PRISMA 2020 review
PRISMA-compliant review

2009 (original PRISMA statement); updated 2020

A PRISMA-based review is a systematic literature review conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Originally published by Moher et al. in 2009 and updated as PRISMA 2020 by Page et al., the framework specifies a 27-item checklist and a four-phase flow diagram covering identification, screening, eligibility, and inclusion — ensuring full transparency and reproducibility in the review process.

Structured reporting framework for systematic reviews

Published studies, trial reports, observational study records

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine, 6(7), e1000097.

systematic-literature-review meta-analysis scoping-review umbrella-review integrative-review rapid-review


PRISMA Checklist

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a 27-item evidence-based checklist published in 2021 (updated from 2009) to standardize reporting of systematic reviews and meta-analyses. Endorsed by over 500 journals, PRISMA is the international standard for evidence synthesis reporting, used across healthcare, psychology, education, and social sciences to ensure transparency and reproducibility.

Systematic review author reporting checklist

consort-reporting-checklist strobe-checklist grade-evidence-profiling cochrane-risk-of-bias


PRISMA-compliant Co-citation analysis

co-citation analysis with PRISMA reporting

2009–2021 (methodological combination emerged in the 2010s)

PRISMA: Moher et al. (2009), updated Page et al. (2021); Co-citation: Henry Small (1973)

PRISMA-compliant co-citation analysis is a systematic bibliometric method that applies the PRISMA 2020 reporting framework to co-citation analysis. It identifies intellectual clusters in a research field by measuring how frequently pairs of documents are cited together, while ensuring full transparency of the literature search, screening decisions, and analytic choices through a pre-registered protocol and standardised flow diagram.

Citation databases (Web of Science, Scopus); co-citation frequency matrices

Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269.

co-citation-analysis bibliometric-analysis bibliographic-coupling systematic-literature-review science-mapping prisma-based-review


PRISMA-compliant Scoping review

PRISMA for Scoping Reviews (PRISMA-ScR) Compliant Scoping Review

PRISMA-ScR scoping review
scoping review with PRISMA-ScR
PRISMA scoping review
transparent scoping review

2018 (PRISMA-ScR extension); 2005 (scoping review framework)

Tricco et al. (PRISMA-ScR); Arksey & O'Malley (scoping review framework)

A PRISMA-compliant scoping review is a scoping review conducted and reported according to the PRISMA for Scoping Reviews (PRISMA-ScR) extension, a 20-item checklist plus explanation published by Tricco et al. in 2018. Scoping reviews map the breadth and volume of evidence on a topic without synthesizing effect sizes; the PRISMA-ScR overlay adds transparency, reproducibility, and reporting completeness standards analogous to those PRISMA provides for systematic reviews and meta-analyses.

Evidence synthesis — scoping review with standardized reporting

Published literature records (titles, abstracts, full texts)

Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., ... & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.

Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

scoping-review systematic-literature-review prisma-based-review umbrella-review mapping-review narrative-review


PRISMA-compliant Umbrella Review

PRISMA-Compliant Umbrella Review of Systematic Reviews

umbrella review with PRISMA
PRIOR-guided umbrella review
overview of reviews
PRISMA umbrella overview

2015 (umbrella review methodology); 2022 (PRIOR reporting extension)

Aromataris et al. (JBI); PRIOR reporting extension by Fusar-Poli et al.

A PRISMA-compliant umbrella review is a structured synthesis of existing systematic reviews and meta-analyses on a topic, conducted and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines — specifically the PRIOR extension developed for umbrella reviews. By operating one level above primary research, it maps the totality of evidence, identifies convergent or contradictory findings across reviews, and quantifies evidence strength at the highest tier of the evidence hierarchy.

Evidence synthesis / review of systematic reviews

Published systematic reviews and meta-analyses (bibliographic records, extracted evidence tables)

Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: Methodological development, conduct and reporting of an umbrella review approach. International Journal of Evidence-Based Healthcare, 13(3), 132–140.

Fusar-Poli, P., Radua, J., & the PRISMA Umbrella Group (2022). PRIOR: Preferred Reporting Items for Umbrella Reviews. Evidence-Based Mental Health, 25(3), 92–99.

umbrella-review systematic-literature-review prisma-based-review meta-analysis scoping-review network-meta-analysis


PRISMA

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a standardized reporting guideline designed to improve the transparency and completeness of systematic reviews and meta-analyses. Introduced in its current form by Page et al. in 2021 as PRISMA 2020, it provides a 27-item checklist and a four-phase flow diagram that together ensure every stage of a review — from database searching through final inclusion — is documented and reproducible.

Page, M. J., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.


PEBS

environmental action and behavior assessment

The Pro-Environmental Behavior Scale (PEBS) measures the frequency and breadth of environmentally responsible actions that individuals perform in their daily lives, including recycling, energy conservation, water conservation, sustainable transportation, sustainable consumption, and environmental activism. Unlike attitude scales that measure beliefs or concerns, the PEBS captures actual or self-reported behaviors—providing a bridge between environmental intentions and demonstrable actions. The scale is essential for evaluating behavior-change interventions, tracking progress toward sustainability goals, and understanding which demographic and psychographic segments adopt environmentally responsible practices.

Self-report frequency and behavior checklist

Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environmental Education Research, 8(3), 239–260.

Markowitz, E. M., Goldberg, L. R., Ashton, M. C., & Lee, K. (2012). Profiling the 'pro-environmental individual': A personality perspective. Journal of Personality, 80(1), 81–111.

new-ecological-paradigm connectedness-to-nature-scale environmental-identity-scale sustainable-consumption-scale


Proactive Personality Scale

The Proactive Personality Scale (PPS) measures individual differences in the inclination to take action to shape one's environment and future. Developed by Bateman and Crant in 1993, it quantifies the stable tendency to anticipate and initiate change rather than react passively. The scale predicts career advancement, entrepreneurial intent, and organizational citizenship behaviors, making it valuable in selection, development, and research contexts.

Bateman, T. S., & Crant, J. M. (1993). The proactive component of organizational behavior: A measure and correlates. Journal of Organizational Behavior, 14(2), 103–118.

Seibert, S. E., Kraimer, M. L., & Crant, J. M. (2001). What do proactive people do? A longitudinal model linking proactive personality and career success. Personnel Psychology, 54(4), 845–874.

Parker, S. K., Bindl, U. K., & Strauss, K. (2010). Making things happen: A model of proactive motivation. Journal of Management, 36(4), 827–856.


PROB-ARAS

PROB-ARAS (Prob-ARAS — Stochastic extension of PROB-ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K. Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Monte Carlo / stochastic weight uncertainty extension of ARAS

Zavadskas, E. K., Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Probabilistic Roadmap

The Probabilistic Roadmap (PRM) method is a motion planning algorithm that builds a pre-computed graph (roadmap) of feasible paths through the configuration space by sampling random configurations and connecting them if collision-free. Introduced by Kavraki et al. in 1996, PRM is powerful for multi-query planning scenarios where many path queries are answered, amortizing roadmap construction cost across many queries.

Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580.

Overmars, M. H., & Svestka, P. (1992). A probabilistic learning approach to motion planning. Proceedings of the Fourth Workshop on Algorithmic Foundations of Robotics, 19-37.

LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press.


Probabilistic Seismic Hazard Analysis

probabilistic earthquake hazard assessment

Probabilistic Seismic Hazard Analysis (PSHA) is a quantitative engineering framework used in civil and geotechnical engineering to estimate the likelihood that ground shaking will exceed a specified intensity level at a site within a given time window. By combining earthquake source geometry, recurrence statistics, and ground-motion attenuation models, PSHA produces hazard curves and maps that inform seismic design codes, infrastructure planning, and risk management decisions.

Seismicity catalogs, fault geometry, ground-motion records

Cornell, C. A. (1968). Engineering seismic risk analysis. Bulletin of the Seismological Society of America, 58(5), 1583–1606.

Kramer, S. L. (1996). Geotechnical Earthquake Engineering. Prentice Hall.

deterministic-seismic-hazard-analysis ground-motion-prediction-equations seismic-risk-assessment fragility-analysis fault-rupture-hazard-analysis monte-carlo-simulation


PROBID

PROBID (Preference Ranking on the Basis of Ideal-Average Distance) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, Z., Rangaiah, G. P., Wang, X. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Multi-ideal distance ranking with harmonic weighting

Wang, Z., Rangaiah, G. P., Wang, X. (2021). Preference ranking on the basis of ideal-average distance method for multi-criteria decision-making. Industrial & Engineering Chemistry Research


Probit Model

probit regression
normit model
Probit Modeli

Greene (textbook treatment); classical discrete-choice modelling

The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).

Standard normal cumulative distribution function (Φ)

Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.

logistic-regression ols-regression panel-fixed-effects instrumental-variables quantile-regression


Problem Areas in Diabetes Scale

diabetes-specific emotional and behavioral problems

The Problem Areas in Diabetes Scale (PAID) is a 20-item self-report measure that assesses emotional and behavioral problems related to diabetes self-management, including worries about complications, regimen burden, social and family challenges, and emotional distress. Originally developed by Polonsky and colleagues in 1995, the PAID has been extensively validated and remains one of the most widely used diabetes-specific emotional assessment tools in research and clinical practice, particularly for identifying psychosocial barriers to optimal diabetes control.

Welch, G. W., Weinger, K., Anderson, B., & Polonsky, W. H. (1997). Responsiveness of the Problem Areas In Diabetes (PAID) questionnaire. Diabetes Care, 20(5), 696–702.

Polonsky, W. H., Anderson, B. J., Lohrer, P. A., Welch, G. W., Jacobson, A. M., Aponte, J. E., & Schwartz, C. E. (1995). Assessment of diabetes-related emotional and behavioral problems: Validation of the Problem Areas In Diabetes Scale. Diabetes Care, 18(10), 1330–1336.


PSUS

PSUS
Smartphone Addiction Scale
Mobile Phone Addiction

smartphone and mobile technology addiction

Zahra Hussain, Mark Griffiths, David Sheffield

The PSUS is a self-report questionnaire measuring compulsive smartphone use, withdrawal symptoms, and loss of control related to mobile devices. Developed by Hussain, Griffiths, and Sheffield in 2017, it targets the growing phenomenon of smartphone addiction in the digital age. The PSUS captures how smartphone dependence differs from general internet addiction, with particular focus on the constant connectivity and notification-driven engagement of mobile devices. Related instruments include the Smartphone Addiction Scale (SAS) by Kwon and colleagues, which focuses on adolescents.

Hussain, Z., Griffiths, M. D., & Sheffield, D. (2017). An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors. Journal of Behavioral Addictions, 6(3), 378–386.

Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013). The Smartphone Addiction Scale: Development and validation of a short version for adolescents. PLOS ONE, 8(12), e83558.

Chen, I. H., Pakpour, A. H., Leung, H., et al. (2020). Severe smartphone addiction and its association with depression, quality of life and spending behavior in adolescents. International Journal of Environmental Research and Public Health, 17(10), 3612.


Process Capability Analysis

Process Capability Indices
Capability Study
Süreç Yeterlilik Analizi
Process Performance Analysis

Process Capability Analysis quantifies how well a manufacturing or business process produces output within specified tolerance limits. Introduced formally by Victor Kane in 1986, it summarises process spread and centering into dimensionless indices — most notably Cp and Cpk — allowing engineers and quality managers to judge whether a stable process is inherently capable of meeting customer or design specifications consistently.

Output: Dimensionless capability ratio (Cp, Cpk)

Process output is normally distributed and in statistical control

Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.


Process Mining

Workflow Mining
Event Log Analysis
Process Discovery
Süreç Madenciliği

Process Mining is a data-driven discipline that extracts knowledge about real-world processes from event logs recorded by information systems. Introduced systematically by Wil van der Aalst, with foundational workflow mining formalized in 2004 and consolidated in the 2016 textbook, the technique bridges data science and process management. It enables organizations to discover how processes actually execute, check whether execution conforms to prescribed models, and diagnose performance bottlenecks — all directly from digital traces.

Input: Event logs (case ID, activity, timestamp)

Output: Process model, conformance statistics, performance metrics

van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer.

van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128–1142.


Process Tracing

Process Tracing is a qualitative research method developed by George and Bennett (2005) for studying causal mechanisms and causal chains within individual cases. It involves examining the sequence of events and decision-making processes within a case to infer whether a hypothesized causal mechanism actually operated. Process tracing aims to strengthen causal inference in case studies by looking beyond correlation to understand how causes produce effects.

Bennett, A., & Checkel, J. T. (Eds.). (2015). Process Tracing: From Metaphor to Analytic Tool. Cambridge University Press.

George, A. L., & Bennett, A. (2005). Case Studies and Theory Development in the Social Sciences. MIT Press.

Fairfield, T., & Charman, A. E. (2017). Explicit causal chains? Evaluating new directions for process tracing. Comparative Political Studies, 50(12), 1584-1607.


Procrastination Assessment Scale for Students (PASS)

The Procrastination Assessment Scale for Students is a comprehensive instrument measuring the frequency of academic procrastination across multiple task types and identifying the underlying reasons for delay. Developed by Solomon and Rothblum in 1984, the PASS provides educators and researchers with actionable data about which academic tasks students avoid and why—information critical for designing targeted interventions to improve academic performance and reduce associated stress.

Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31(4), 503-509.

Ferrari, J. R., Johnson, J. L., & McCown, W. G. (1995). Procrastination and task avoidance: Theory, research, and treatment. Plenum Press.

academic-burnout-scale test-anxiety-inventory academic-resilience-scale academic-help-seeking-scale study-skills-assessment


PIS

Healthcare Professional Identity
Disciplinary Identity Assessment

The PIS is a self-report questionnaire measuring healthcare students' sense of professional identity, belonging, and commitment to their chosen discipline. Developed by Adams and colleagues in 2006, the PIS assesses the degree to which students have internalized professional roles, values, behaviors, and career commitment. The scale measures both cognitive elements (knowledge of professional standards and scope of practice) and emotional elements (sense of belonging, pride in discipline). The PIS is used in healthcare education to track professional identity development over training, identify students at risk of attrition, and evaluate the impact of socialization experiences on disciplinary commitment.

Adams, K., Hean, S., Sturgis, P., & Clark, J. M. (2006). Investigating the factors influencing professional identity of first-year health and social care students. Learn Health Soc Care 5(2): 55–68.

Weidman, J. C. & Stein, E. L. (2003). Socialization of doctoral students to academic careers. New Dir High Educ 101: 65–78.

ripls clinical-learning-environment-scale interprofessional-collaboration-scale reflective-practice-questionnaire


Profile of Mood States

Douglas McNair, Maurice Lorr, Lorne Droppleman

The POMS is a 65-item self-report questionnaire that measures six dimensions of mood: Tension, Depression, Anger, Vigor, Fatigue, and Confusion. Developed by McNair, Lorr, and Droppleman in 1971, it has become a cornerstone instrument in sport psychology for monitoring athlete psychological state before competition, during training, and in response to interventions.

Self-report mood adjectives questionnaire

McNair, D. M., Lorr, M., & Droppleman, L. F. (1971). Profile of Mood States. Educational and Industrial Testing Service (EDITS).

McNair, D. M., Lorr, M., & Droppleman, L. F. (1992). Manual for the Profile of Mood States (Revised). San Diego, CA: EDITS.

competitive-state-anxiety-inventory sport-confidence-inventory mental-toughness-questionnaire task-ego-orientation-sport


Prognostics and Remaining Useful Life

Prognostics and Remaining Useful Life (RUL) Prediction

RUL
Remaining useful life
PHM
Prognostics and Health Management

Condition monitoring and predictive maintenance

Prognostics and Health Management (PHM) is a methodology for predicting the remaining useful life (RUL) of equipment by monitoring its condition and extrapolating degradation trends. Unlike reactive maintenance (wait for failure) or preventive maintenance (fixed schedules), prognostics enable predictive maintenance: act only when failure is imminent. Formalized in the 2000s by researchers including George Vachtsevanos, RUL prediction integrates sensor data, degradation models, and uncertainty quantification to inform maintenance planning and reduce downtime.

Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley.

Saxena, A., Celaya, J., Balaji, B., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2010). Metrics for evaluating the accuracy of prognostic techniques. International Journal of Prognostics and Health Management, 1(1), 1-20.

Goebel, K., Saha, B., & Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. IEEE Aerospace Conference, 1-11.

Si, X. S., Wang, W., Hu, C. H., & Chen, M. Y. (2012). Remaining useful life estimation based on stochastic degradation models. Reliability Engineering & System Safety, 99, 146-154.

rainflow-counting highly-accelerated-life-testing first-order-reliability-method finite-element-model-updating


Program Evaluation

evaluation research
program assessment
educational evaluation
systematic program evaluation

1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)

Michael Scriven; Daniel Stufflebeam; Peter Rossi

Program evaluation is a systematic, empirically grounded process of collecting and analyzing information about a program to determine its merit, worth, or significance. Applied across education, public health, social services, and policy, it addresses questions such as whether a program is reaching its target population, whether it is being implemented as designed, and whether it is producing the intended outcomes. It draws on both quantitative and qualitative methods and serves accountability, improvement, or knowledge-generation purposes.

Quantitative outcomes data, qualitative interviews, documents, observations

Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage.

Stufflebeam, D. L., & Shinkfield, A. J. (2007). Evaluation Theory, Models, and Applications. Jossey-Bass.

educational-action-research curriculum-analysis design-based-research needs-assessment logic-model mixed-methods-research


PG-13
Prigerson PG-13
Prolonged Grief Symptom Scale

The Prolonged Grief Disorder Scale (PG-13) is a 13-item diagnostic assessment tool developed by Prigerson and Maciejewski to operationalize the DSM-5-TR diagnostic criteria for Prolonged Grief Disorder. Designed as a structured clinical instrument, the PG-13 directly maps onto the symptomatic, cognitive, and functional criteria required for diagnosis, making it invaluable in clinical and research settings where standardized diagnostic assessment is needed.

Prigerson, H. G., & Maciejewski, P. K. (2008). Prolonged grief disorder: Defining the disorder and addressing its clinical and public health significance. Psychotherapy and Psychosomatics, 77(6), 365–376.

inventory-complicated-grief texas-revised-inventory-grief hogan-grief-reaction-checklist anticipatory-grief-scale


PROMETHEE-GAIA

Preference Ranking Organization Method for Enrichment Evaluations with Geometric Analysis for Interactive Aid (PROMETHEE-GAIA)

PROMETHEE-GAIA combines two complementary tools: PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) for ranking alternatives through pairwise preference modeling, and GAIA (Geometric Analysis for Interactive Aid) for visual representation and sensitivity analysis. While PROMETHEE produces a ranking, GAIA displays the relative importance of criteria and the position of alternatives in a 2D plane, facilitating stakeholder understanding and decision adjustment.

Pairwise preference-based ranking with visual analysis

Brans, J. P., & Vincke, P. (1985). A preference ranking organization method: The PROMETHEE method for MCDM. Management Science, 31(6), 647-656.

Brans, J. P., & Mareschal, B. (2005). PROMETHEE methods. In J. Figueira, S. Greco, & M. Ehrgott (Eds.), Multiple criteria decision analysis: State of the art surveys (pp. 163-195). Springer.

promethee electre topsis vikor graph-based-ranking


PROMETHEE-I

PROMETHEE-I (PROMETHEE I — Preference Ranking Organisation METHod for Enrichment Evaluations I (partial ranking)) is a ranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, P., Mareschal, B. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking via positive/negative flows (partial preorder)

Brans, J. P., Vincke, P., Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research


PROMETHEE-II

PROMETHEE-II (PROMETHEE II — Preference Ranking Organisation Method for Enrichment of Evaluations II) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, P. in 1985. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking (pairwise preference flows — complete ranking)

Brans, J. P., Vincke, P. (1985). A preference ranking organisation method (The PROMETHEE method for multiple criteria decision-making). Management Science

ahp anp bwm critic delphi entropy fucom swara


PROMETHEE-III

PROMETHEE-III (PROMETHEE III — Preference Ranking Organisation METHod for Enrichment Evaluations III (interval ranking)) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P. Vincke, P. Mareschal, B. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking via interval flows derived from net-flow mean and dispersion


PROMETHEE-V

PROMETHEE-V (PROMETHEE V — PROMETHEE with Integer Programming Constraints) is a ranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P. Mareschal, B. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

PROMETHEE II net flows maximized over a subset selection IP problem

Brans, J. P., Mareschal, B. (1992). PROMETHEE V: MCDM problems with segmentation constraints. INFOR: Information Systems and Operational Research


PROMETHEE-VI

PROMETHEE-VI (PROMETHEE VI — Walking Weights Sensitivity) is a ranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P. Mareschal, B. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Sensitivity analysis of PROMETHEE rankings across all feasible weight combinations

Brans, J. P., Mareschal, B. (1995). The PROMETHEE VI procedure: how to differentiate hard from soft multicriteria problems. Journal of Decision Systems


PROMETHEE

PROMETHEE (PROMETHEE II — Preference Ranking Organisation METHod for Enrichment of Evaluations) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, Ph., Mareschal, B. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Brans, J. P., Vincke, Ph., Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research


PROMIS

PROMIS measures
NIH PROMIS
Computer Adaptive Testing PROMIS

National Institutes of Health (NIH) and National Center for Health Statistics (NCHS)

The Patient-Reported Outcomes Measurement Information System (PROMIS) is a comprehensive, flexible system of patient-reported outcome measures developed by the National Institutes of Health. Launched in 2010, PROMIS measures health across multiple domains using both fixed-item forms and computer-adaptive testing (CAT). It has become the gold standard for outcomes measurement in clinical trials and health systems research.

Computer-adaptive testing and fixed-length patient-reported outcome measures

Cella, D., Yount, S., Rothrock, N., et al. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years. Medical Care, 45(Suppl 1), S3–S11.

Reeve, B. B., Hays, R. D., Bjorner, J. B., et al. (2013). Psychometric evaluation and calibration of health-related quality of life item banks. Medical Care, 45(Suppl 3), S22–S31.

National Institutes of Health. (2023). PROMIS Measures Overview. National Institute of Child Health and Human Development.


Prompt Engineering

Prompt Engineering (Instruction Design for Large Language Models)

Yönerge Mühendisliği (Prompt Engineering)

2020 (few-shot prompting); 2022 (chain-of-thought)

Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)

Prompt engineering is the practice of crafting structured natural-language instructions — prompts — to elicit targeted outputs from large language models (LLMs). Formalised by Brown et al. (2020) in the context of GPT-3 and extended by Wei et al. (2022) with chain-of-thought prompting, it encompasses four main strategies: zero-shot, few-shot, chain-of-thought, and tree-of-thought. Rather than re-training a model, the analyst shapes the model's behaviour entirely through the design of the input text.

NLP pipeline — structured instruction design for large language models

Zero-shot / few-shot / chain-of-thought (CoT) / tree-of-thought (ToT)

Input: Natural-language text prompt sent to an LLM API

Output: Model-generated text (classification label, reasoning trace, summary, structured data, etc.)

Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901.

Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems (NeurIPS), 35.

retrieval-augmented-generation few-shot-text-classification zero-shot-classification chain-of-thought-reasoning text-classification natural-language-generation gpt-finetuning lora-peft


Propaganda Detection

propaganda and manipulation detection
propaganda technique detection
Propaganda ve Manipülasyon Tespiti

Propaganda detection is a natural-language-processing task that automatically identifies and labels persuasion and manipulation techniques in text — such as loaded language, oversimplified solutions, bandwagon appeals, and glittering generalities. It builds on the fine-grained propaganda analysis introduced by Da San Martino et al. (2019), turning rhetorical manipulation into structured, technique-level labels.

Loaded language, oversimplification, bandwagon, glittering generalities, and other propaganda devices

Output: Per-span or per-document propaganda-technique labels

Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP.

Rashkin, H. et al. (2017). Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. EMNLP.

sentiment-analysis emotion-detection frame-analysis-nlp text-classification


Propeller Lifting Line

lifting line theory
propeller design method
Goldstein method

Propeller lifting line theory is a mathematical framework for analyzing and designing ship propellers by modeling each blade as a lifting line with circulation distribution. Developed by Sydney Goldstein in 1929 and refined by Kerwin and others, the method accounts for blade loading, wake effects, and propeller interactions. Lifting line theory provides efficient predictions of propeller thrust, torque, and efficiency and remains standard in preliminary propeller design and optimization.

Goldstein, S. (1929). On the vortex theory of screw propellers. Proceedings of the Royal Society of London. Series A, 123(792), 440–465.

Kerwin, J. E., & Lee, C. S. (1986). Prediction of steady and unsteady marine propeller performance by numerical lifting-surface theory. SNAME Transactions, 94, 332–377.

Phillips, A. B., Turnock, S. R., & Furlong, M. (2010). Comparisons of CFD simulations of cavitating propellers with experiment. In Proceedings of the First International Symposium on Marine Propulsors (smp'09).


Propensity Score Matching in Education Research

Propensity Score Matching Applied to Education Research

PSM in education
educational PSM
PSM for program evaluation in schools
propensity matching education

1983 (foundational); education adoption widespread from late 1990s

Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)

Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible.

Quasi-experimental / matching-based causal inference

Observational cross-sectional or panel data with individual-level student, teacher, or school records

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

propensity-score-weighting difference-in-differences coarsened-exact-matching regression-discontinuity-design inverse-probability-weighting matching-estimator


Propensity Score Matching

PSM
propensity score weighting
covariate balance

Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding. Multivariate Behavioral Research, 46(3), 399–424.

Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.


Propensity Score Weighting in Education Research

Propensity Score Weighting for Causal Inference in Education Research

inverse probability weighting in education

1983 (theory); widely adopted in education research from 2000s

Rosenbaum & Rubin (foundational theory, 1983); Thoemmes & Kim (education-focused review, 2011)

Propensity score weighting (PSW) is a quasi-experimental technique that reweights observational samples so that treated and comparison students look similar on measured background characteristics, allowing credible causal estimates of educational interventions — such as program participation, instructional method, or school type — without random assignment.

Observational cross-sectional or longitudinal data with a binary or multi-valued treatment

Thoemmes, F. J., & Kim, E. S. (2011). A Systematic Review of Propensity Score Methods in the Social Sciences. Multivariate Behavioral Research, 46(1), 90-118.

propensity-score-matching difference-in-differences instrumental-variables regression-discontinuity covariate-adjustment inverse-probability-weighting


Propensity Score Weighting

PSW
inverse probability weighting
IPW
propensity-based weighting

1983 (propensity score); 2003 (efficient IPW estimator)

Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)

Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).

Observational cross-sectional or panel data with binary or multi-valued treatment

Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.

propensity-score-matching inverse-probability-weighting doubly-robust-estimation difference-in-differences coarsened-exact-matching entropy-balancing


Prophet

Prophet Decomposable Time Series Forecasting Model

Prophet — Facebook/Meta Zaman Serisi Modeli

Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale.

Decomposable (structural) time series model

Bayesian curve fitting (MAP or full posterior)

Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.

Taylor, S. J. & Letham, B. (2017). Prophet: Forecasting at Scale [Software]. Facebook/Meta.

ets-model holt-winters structural-time-series state-space-model ols-regression


Proportion Test

z-test for proportions
two-sample proportion test
one-proportion z-test
Oran Testi — z Testi (Oranlar)

Karl Pearson / classical large-sample z approximation

The proportion test (z-test for proportions) is a parametric hypothesis test that compares one or two sample proportions against a reference value or each other. Grounded in the large-sample normal approximation formalized by Fleiss, Levin, and Paik (2003), it is the standard tool for binary outcome comparisons when samples are large enough for the central limit theorem to apply.

Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley.

chi-square-test binomial-test fisher-exact-test logistic-regression independent-t-test


Proportional Cluster Sampling

Proportional Cluster Sampling (Probability Proportional to Size)

probability proportional to size sampling

Formalized by William G. Cochran and Leslie Kish

Proportional cluster sampling selects naturally occurring groups (clusters) from a population with probability proportional to each cluster's size, so that larger clusters have a higher chance of selection while every individual element retains an equal overall inclusion probability. This design efficiently handles large, geographically dispersed populations and is the backbone of national health, education, and social surveys worldwide.

Population registers, census frames, or administrative lists with cluster size information

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

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

cluster-sampling stratified-sampling multistage-sampling systematic-sampling proportional-stratified-sampling simple-random-sampling


Proportional Convenience Sampling

quota-constrained convenience sampling
representative convenience sampling
proportionate accidental sampling
PCS

Developed within mainstream sampling methodology; no single originator

Proportional convenience sampling is a non-probability technique that recruits participants through convenience while constraining each subgroup's share in the final sample to match its known proportion in the target population. It trades pure random selection for feasibility, but partially compensates by ensuring the sample's compositional profile mirrors the population on one or more key variables such as gender, age group, or academic year.

Non-probability sampling with proportional allocation constraint

Any (quantitative, qualitative, mixed); commonly survey or observational data

Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 215–217.

convenience-sampling quota-sampling proportional-stratified-sampling purposive-sampling proportional-quota-sampling simple-random-sampling


Proportional Multistage Sampling

multistage probability proportional to size sampling

proportionate multistage cluster sampling

Leslie Kish; William G. Cochran (theoretical foundations)

Proportional multistage sampling is a probability sampling design that selects units across two or more hierarchical stages — for example, regions, then districts, then households — where the number of units drawn at each stage is proportional to the size of each higher-level unit. By weighting selection probabilities to match cluster size, it produces self-weighting samples that closely mirror the population structure and simplify variance estimation.

Population registers, administrative lists, geographic/cluster units

Kish, L. (1965). Survey Sampling. John Wiley & Sons. (Chapters 6–7 on multistage and PPS designs.)

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. (Chapter 11 on multistage sampling with proportional allocation.)

multistage-sampling proportional-stratified-sampling cluster-sampling systematic-sampling proportional-cluster-sampling stratified-sampling


Proportional Navigation

Proportional Navigation (PN) is a guidance law that generates command accelerations proportional to the rate of change of the line-of-sight angle between a pursuer and target. Introduced by Lin-Hsiung Chu in the 1950s, it became the foundation of modern missile guidance systems. PN solves the pursuit-evasion problem by ensuring that the pursuer intercepts a moving target with minimal computational overhead.

Knox, W. P. (1971). On optimal proportional navigation. IEEE Transactions on Aerospace and Electronic Systems, AES-7(3), 417–426.

Guelman, M. (1971). Proportional navigation with a maneuvering target. IEEE Transactions on Aerospace and Electronic Systems, AES-8(3), 364–371.

Lin, C. F., & Tsai, L. L. (2007). Guidance Laws for Missiles. In Modern Control Systems for Unmanned Aerial Vehicles. Butterworth-Heinemann.


Proportional Purposive Sampling

proportional criterion sampling
quota-proportional purposive sampling
representational purposive sampling

Derived from purposive sampling tradition (Patton); formalized in mixed-methods literature

Proportional purposive sampling combines the intentional case selection of purposive sampling with proportional allocation across subgroups. Researchers first determine how each meaningful subgroup (e.g., gender, school type, professional role) is represented in the population, then deliberately select participants from each subgroup in those same proportions — using purposive judgment to ensure each selected case is information-rich and relevant to the research question.

Non-probability sampling with proportional allocation

Mixed or qualitative data; units selected by judgment within proportional cells

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.

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

purposive-sampling proportional-stratified-sampling quota-sampling maximum-variation-sampling stratified-purposive-sampling theoretical-sampling


Proportional Simple Random Sampling

probability-proportional simple random sampling

Mid-20th century (formalized ~1950s–1970s)

William G. Cochran and survey statisticians (classical probability sampling tradition)

Proportional simple random sampling is a probability-based sampling technique in which units are drawn at random from each subgroup of the population in numbers proportional to each subgroup's share of the total population. This ensures the resulting sample mirrors the population's composition across key subgroups, while retaining the randomness and unbiasedness of simple random sampling within each group.

Quantitative; requires a sampling frame with known population sizes per subgroup

Lohr, S. L. (2009). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.

simple-random-sampling proportional-stratified-sampling stratified-sampling systematic-sampling cluster-sampling weighted-sampling


Proportional Stratified Sampling

proportional allocation stratified sampling

proportionate stratified random sampling

1953–1965 (formalized in survey sampling literature)

Proportional stratified sampling divides the target population into non-overlapping strata (subgroups defined by a key characteristic such as age band, region, or gender) and then draws a simple random sample from each stratum so that each stratum's share of the total sample matches its share of the total population. Because each subgroup is represented in exact proportion to its population weight, the resulting sample mirrors the population structure closely without requiring post-hoc weighting adjustments.

Population frame with stratum membership data; quantitative or qualitative outcome variables

stratified-sampling disproportional-stratified-sampling simple-random-sampling cluster-sampling systematic-sampling multistage-sampling


Proportional Systematic Sampling

proportional 1-in-k sampling
equal-probability systematic sampling
proportionate systematic selection
PPS systematic sampling

Codified in classical survey sampling theory; see Cochran (1977)

Proportional systematic sampling combines systematic (every k-th element) selection with proportional allocation across subgroups, ensuring that each stratum contributes sample units in proportion to its share of the total population. The result is an equal-probability design that is administratively simple, spreads the sample evenly across an ordered frame, and eliminates the need for post-hoc weighting when strata are sampled at a uniform rate.

Finite population lists (sampling frames) with known stratum sizes

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

systematic-sampling proportional-stratified-sampling simple-random-sampling stratified-sampling cluster-sampling multistage-sampling


Proportional Weighted Sampling

Mid-20th century (formalized 1950s–1960s)

Proportional weighted sampling is a probability-based survey design in which each subgroup (stratum or cluster) of the population is sampled and weighted in proportion to its true size in the population. By assigning sampling weights that mirror the actual composition of the population, the method ensures unbiased estimates without the need for post-hoc reweighting, and produces efficient estimates when variance within subgroups is relatively homogeneous.

Population registry data, survey frame with known subgroup sizes

stratified-sampling weighted-sampling proportional-stratified-sampling cluster-sampling systematic-sampling multistage-sampling


Proprioception Assessment

kinesthetic assessment
joint position sense testing

Proprioceptive assessment is a bedside neurological examination evaluating the sense of joint position and movement, mediated by mechanoreceptors in muscles, tendons, and joints. Clinical testing of proprioception is essential for comprehensive neurological evaluation in conditions affecting sensory function, coordination, and balance, helping clinicians identify dorsal column disease, peripheral neuropathy, or cerebellar dysfunction.

Sherrington, C. S. (1906). The integrative action of the nervous system. Yale University Press.

Goble, D. J. (2010). Proprioceptive acuity assessment via joint position matching: From basic science to general clinical application. Physical Therapy Reviews, 15(1), 36-46.


PROSA-C

PROSA-C (Sustainability Criteria-Level Preference Ranking) is a ranking multi-criteria decision-making (MCDM) method introduced by Ziemba, P. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

TOPSIS extended with sustainability coefficients at criterion level

Ziemba, P. (2019). PROSA-C: preferences-based sustainability assessments at the criteria level of MCDA models. Sustainability


Prospective Case-Control Study

nested case-control (prospective variant)

1970s–1990s (formalized alongside nested case-control methods)

Evolved from classical retrospective case-control methodology; prospective embedding attributed to modern epidemiological practice (Rothman, Greenland, and others, late 20th century)

A prospective case-control study embeds the case-control logic within a defined cohort followed forward in time. Cases are identified as they occur, rather than looked up in records after the fact, and controls are sampled from the same prospectively monitored base population. This forward-looking approach allows collection of exposure data before outcome ascertainment, reducing recall bias — the principal weakness of the classic retrospective case-control design — while retaining the efficiency gains of sampling controls rather than enrolling a full cohort.

Prospectively collected individual-level exposure and outcome data; incident cases and matched controls from a defined base population

Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.

case-control-study nested-case-control cohort-study prospective-cohort-study case-crossover-design randomized-clinical-trial


Prospective Case-Crossover Design

Prospective Case-Crossover Epidemiological Design

1991 (base design); prospective variant described in late 1990s–2000s

Malcolm Maclure (case-crossover concept); prospective variant established by subsequent methodologists including Navidi and Weinhandl

The prospective case-crossover design is an observational epidemiological study in which each case serves as their own control. Unlike the retrospective variant, exposures are recorded in real time as participants are followed forward, eliminating recall bias. It is particularly suited to investigating transient environmental or behavioral triggers of acute events such as myocardial infarction, asthma attacks, or road-traffic injuries.

Observational epidemiological study design

Time-stamped exposure and event data collected prospectively; repeated measures on the same individual

Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.

Navidi, W., & Weinhandl, E. (2002). Risk set sampling strategies for case-crossover studies. Epidemiology, 13(1), 100–105.

case-crossover-design case-control-study prospective-cohort-study self-controlled-case-series time-series-analysis repeated-measures-design


Prospective Case Series

prospective case series study
forward-looking case series
prospective uncontrolled study
prospective observational case series

Late 19th century onward; formalized in modern clinical epidemiology by the 1970s–1980s

Evolved from clinical case reporting traditions in 19th–20th century medicine

A prospective case series is an observational study design in which a group of patients with a particular condition, exposure, or intervention is identified in advance and followed forward in time according to a pre-specified protocol. Data on outcomes, adverse events, and clinical course are collected as they occur, yielding higher data quality and temporal clarity than retrospective designs. No control group is included, so causal inference is limited, but the design is valuable for characterizing natural disease history, early safety signals, and feasibility of new interventions.

Clinical patient records, follow-up measurements, laboratory and imaging data collected prospectively

Meinert, C. L. (1996). Clinical Trials: Design, Conduct, and Analysis. Oxford University Press.

Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., Riley, D., & the CARE Group. (2016). The CARE Guidelines: Consensus-based Clinical Case Reporting Guideline Development. Global Advances in Health and Medicine, 2(5), 38-43.

case-series prospective-cohort-study retrospective-case-series case-report cohort-study diagnostic-accuracy-study


Prospective Cohort Study

longitudinal cohort study
prospective follow-up study
incidence study
prospective observational cohort

1950s (systematic application); conceptual roots earlier

Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks

A prospective cohort study assembles a group of participants who are free of the outcome of interest at baseline, measures their exposures, and then follows them forward in time to record who develops the outcome. By collecting exposure data before outcomes occur, it establishes a clear temporal sequence that supports causal inference — a major advantage over retrospective designs. It is the cornerstone observational method in epidemiology and clinical research.

Baseline exposure measurements followed by prospective outcome surveillance (binary or time-to-event outcomes)

Doll, R., & Hill, A. B. (1954). The mortality of doctors in relation to their smoking habits. British Medical Journal, 1(4877), 1451-1455.

cohort-study retrospective-cohort-study case-control-study randomized-clinical-trial survival-analysis nested-case-control


Prospective Competing Risks Analysis

prospective subdistribution hazard analysis

prospective cause-specific hazard analysis

forward-looking competing events analysis

1978–1999 (foundational frameworks; prospective application standard by 2000s)

Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978)

Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present.

Observational analytic study with event-time statistical analysis

Time-to-event data from prospective follow-up; binary or categorical competing event outcomes

Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.

Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine, 26(11), 2389–2430.

competing-risks-analysis prospective-survival-analysis prospective-cohort-study cox-proportional-hazards kaplan-meier-analysis prospective-kaplan-meier-analysis


Prospective Cox proportional hazards

Prospective Cox Proportional Hazards Regression

prospective Cox regression
Cox PH prospective study
prospective survival regression
prospective hazard modeling

1972 (Cox model); widespread prospective application from late 1970s

David R. Cox (model); applied prospectively in large cohort studies from 1970s onward

Prospective Cox proportional hazards regression combines a forward-looking cohort design — in which participants are enrolled before outcomes occur and followed over time — with Cox's semi-parametric survival model. The method estimates how baseline covariates measured at enrollment influence the rate at which participants experience a time-to-event outcome, while preserving the temporal direction required for causal inference. It is one of the most widely used analytical frameworks in clinical epidemiology and chronic disease research.

Semi-parametric survival regression applied to prospectively collected time-to-event data

Prospectively collected time-to-event data with censoring, continuous and categorical covariates

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.

Schoenfeld, D. (1982). Partial residuals for the proportional hazards regression model. Biometrika, 69(1), 239–241.

cox-proportional-hazards prospective-cohort-study kaplan-meier-analysis prospective-kaplan-meier-analysis competing-risks-analysis survival-analysis


Prospective Diagnostic Accuracy Study

prospective DTA study
prospective test accuracy study
forward-looking diagnostic study
prospective index test evaluation

Formalized 2000s; practice dates to mid-20th century

Established through STARD initiative (Bossuyt, Reitsma et al., 2000s)

A prospective diagnostic accuracy study enrolls participants before any test results are known and follows them forward in time to evaluate how well an index test (the test under evaluation) distinguishes individuals with and without a target condition, using a reference standard applied independently. Key accuracy metrics include sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve. The prospective design reduces many biases inherent in retrospective test evaluations.

Index test results, reference standard results, patient-level clinical data

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & Cohen, J. F. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527.

Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., ... & Kleijnen, J. (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8), 529-536.

diagnostic-accuracy-study retrospective-diagnostic-accuracy-study cohort-study screening-test-evaluation cross-sectional-epidemiological-study randomized-clinical-trial


Prospective Dose-Response Analysis

Prospective Dose-Response Analysis in Epidemiological Research

prospective exposure-response analysis
prospective trend analysis
forward-looking dose-response study
prospective gradient analysis

1965 (Hill's criteria); widely applied through 1980s–present

Bradford Hill (causal criteria including dose-response, 1965); formalized in modern epidemiology by Rothman, Greenland and others

Prospective dose-response analysis is an epidemiological approach that measures exposure levels in a defined population before outcomes occur, then quantifies how the risk or magnitude of an outcome changes systematically as exposure increases. By collecting exposure data prospectively, researchers can establish temporal sequence, reduce recall bias, and assess whether a biological gradient — one of Hill's classic causal criteria — exists between the agent of interest and a health outcome.

Longitudinal exposure measurements and outcome data collected prospectively

Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309.

dose-response-analysis prospective-cohort-study survival-analysis cox-proportional-hazards regression-analysis exposure-assessment


Prospective Ecological Study

Prospective Ecological Epidemiological Study

prospective ecologic study
prospective aggregate-level study
prospective group-level study
ecological cohort study

1950s–1970s (ecological epidemiology); prospective variant widely applied from 1980s onward

Ecological study design formalised in epidemiology mid-20th century; prospective variant established through environmental and chronic disease research

A prospective ecological study is an observational epidemiological design in which groups — not individuals — serve as the unit of analysis, and exposure data are collected going forward in time before outcomes are measured. Investigators define geographically, politically, or socially bounded populations, characterise their aggregate exposures at baseline, then ascertain group-level outcomes (disease rates, mortality rates) at one or more later time points. Because exposure precedes outcome measurement, this design provides stronger temporal evidence than retrospective ecological studies.

Aggregate (group-level) exposure and outcome data collected prospectively over time

Morgenstern, H. (1998). Ecological studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.

ecological-study cohort-study prospective-cohort-study cross-sectional-epidemiological-study time-series-analysis multilevel-analysis


Prospective Nested Case-Control

nested case-control within prospective cohort

D.C. Thomas (formal description); building on Mantel (1973) and Liddell, McDonald & Thomas (1977)

A prospective nested case-control study enrolls a cohort before disease onset, follows participants forward in time, and then — once cases develop — samples matched controls from those still at risk at the time each case occurs. By embedding the case-control comparison inside a prospective cohort, the design combines the causal clarity of longitudinal follow-up with the cost efficiency of analysing only a fraction of the cohort's stored specimens or records.

Time-to-event data, biomarkers, prospectively collected exposure data

Thomas, D.C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. By F.D.K. Liddell, J.C. McDonald, and D.C. Thomas. Journal of the Royal Statistical Society, Series A, 140(4), 469-491.

Wacholder, S., McLaughlin, J.K., Silverman, D.T., & Mandel, J.S. (1992). Selection of controls in case-control studies: I. Principles. American Journal of Epidemiology, 135(9), 1019-1028.

nested-case-control cohort-study prospective-cohort-study case-control-study case-cohort-study survival-analysis


Prospective Phase IV Study

Prospective Phase IV Post-Marketing Study

prospective post-marketing surveillance study

prospective post-authorization safety study

1970s–1980s (formalized in post-marketing regulatory frameworks)

Regulatory and pharmaceutical research community (ICH E2E, EMA PASS guidelines)

A prospective Phase IV study is a post-marketing investigation conducted after a drug, device, or intervention has received regulatory approval, following participants forward in real time to collect safety, effectiveness, and utilization data under routine clinical practice conditions. Unlike retrospective designs that mine existing records, prospective enrollment allows pre-specified data collection, defined follow-up windows, and direct measurement of outcomes as they occur, making it central to post-authorization safety surveillance and long-term effectiveness research.

Observational / interventional post-marketing study design

Longitudinal patient-level data collected prospectively (clinical records, registries, patient-reported outcomes)

Strom, B.L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). Wiley.

phase-iv-study cohort-study prospective-cohort-study pharmacovigilance randomized-clinical-trial case-control-study


Prospective Randomized Clinical Trial

Prospective Randomized Controlled Clinical Trial

Prospective RCT
randomized controlled trial
RCT
controlled clinical trial

Austin Bradford Hill / Medical Research Council

A prospective randomized clinical trial (RCT) is an experimental study in which participants are assigned to intervention or control groups by chance before any outcomes are observed, then followed forward in time. Random allocation eliminates systematic selection bias, making this design the gold standard for establishing causal efficacy of treatments in medicine and clinical research.

Experimental / interventional study design

Prospectively collected clinical outcome data (continuous, binary, time-to-event)

Medical Research Council (1948). Streptomycin treatment of pulmonary tuberculosis: a Medical Research Council investigation. British Medical Journal, 2(4582), 769–782.

Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.

randomized-clinical-trial cohort-study adaptive-randomized-clinical-trial crossover-trial phase-iii-clinical-trial pragmatic-randomized-clinical-trial


Prospective Retrospective Memory Questionnaire

The Prospective and Retrospective Memory Questionnaire (PRMQ) is a 16-item self-report instrument designed to measure subjective memory complaints across two distinct memory domains: prospective memory (remembering to do things in the future) and retrospective memory (remembering past events and information). Developed by Crawford and colleagues at the University of Edinburgh in 2003, the PRMQ provides a brief, validated tool for assessing everyday memory lapses and their impact on functional well-being in both clinical and non-clinical populations.

Self-report questionnaire of prospective and retrospective memory complaints

Crawford, J. R., Smith, G., Maylor, E. A., Della Sala, S., & Logie, R. H. (2003). The Prospective and Retrospective Memory Questionnaire (PRMQ): Normative data and latent structure in a large non-clinical sample. Memory, 11(3), 261-275.

Smith, G., Della Sala, S., Logie, R. H., & Maylor, E. A. (2000). Prospective and retrospective memory in normal ageing and dementia: A questionnaire study. Memory, 8(5), 311-321.

Kliegel, M., Ramuschkat, G., & Martin, M. (2010). Complex prospective memory in younger and older adults: Does the delay of the intended action make a difference? International Journal of Psychology, 45(3), 210-217.

cognitive-failures-questionnaire trail-making-test frontal-assessment-battery mmse adas-cog


Prospective Screening Test Evaluation

Prospective Screening Test Evaluation Study

prospective diagnostic accuracy study
prospective test performance study
forward-looking screening validation
prospective DTA study

Formalized through diagnostic accuracy methodology (Sackett, Haynes, Tugwell; STARD initiative)

A prospective screening test evaluation enrolls participants before the outcome is known, applies the screening test and the reference standard in temporal sequence, and measures how accurately the test identifies individuals with or without the target condition. This forward-looking design minimizes workup bias and spectrum bias, producing estimates of sensitivity, specificity, and predictive values that are more generalizable to real clinical or public-health screening contexts than retrospective alternatives.

Index test results, reference standard results, participant follow-up data

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., et al. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527.

Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.

screening-test-evaluation diagnostic-accuracy-study prospective-cohort-study prospective-diagnostic-accuracy-study cohort-study cross-sectional-epidemiological-study


Prospective Survival Analysis

prospective time-to-event analysis
prospective failure-time analysis
forward-looking survival study
prospective event-time study

1958–1972 (foundational methods); prospective design emphasis formalized by 1980s

Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology

Prospective survival analysis is a longitudinal study design in which participants are enrolled before the event of interest occurs, followed forward in time under standardised conditions, and analysed using survival-analytic methods to estimate the time until a defined clinical endpoint — such as death, disease recurrence, or treatment failure. Because data are collected prospectively, exposure and covariate information are recorded before outcomes are known, substantially reducing recall and selection bias relative to retrospective approaches.

Longitudinal observational or experimental study design with time-to-event analysis

Prospectively collected time-to-event data (event indicator, follow-up time, covariates)

Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.

Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.

survival-analysis kaplan-meier-analysis cox-proportional-hazards prospective-cohort-study competing-risks-analysis randomized-clinical-trial


Protection Relay Coordination

Protection Relay Coordination and Selective Clearing

relay coordination study
protection scheme design
selective overcurrent protection

Protection relay coordination ensures that when a fault occurs, the relay nearest to the fault operates first, isolating only the faulted section while keeping healthy portions of the network energized. This selective clearing strategy minimizes service disruption and is achieved by carefully coordinating pickup currents and time delays across a series of relays. It is fundamental to reliable power system operation.

Mason, C. R. (1956). The Art and Science of Protective Relaying. General Electric.

Phadke, A. G., & Thorp, J. S. (2008). Adaptive Relaying and Protection. Academic Press.

IEEE Std 379-2000: IEEE Guide for the Protection of AC Electric Machinery Having Rated 15 MVA and Above.

fault-analysis-power-system power-flow-analysis smart-grid-state-estimation motor-drive-efficiency-analysis


Proteomics Analysis

proteomics
mass spectrometry-based proteomics
shotgun proteomics
quantitative proteomics

1994–2003 (term coined 1994; shotgun proteomics established early 2000s)

Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)

Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps findings onto biological pathways. It complements transcriptomics by capturing post-translational regulation and actual protein abundance, and is central to biomarker discovery, drug-target identification, and systems biology.

Mass spectrometry raw files (DDA, DIA), protein FASTA databases

Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19–50.

Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198–207.

metabolomics-analysis rna-seq-differential-expression multi-omics-proteomics-analysis pathway-enrichment-analysis gene-set-enrichment-analysis sequence-alignment


Protocol-based Meta-analysis

pre-registered meta-analysis
prospective meta-analysis
registered meta-analysis
protocol-driven meta-analysis

1990s–2015 (Cochrane established 1993; PROSPERO launched 2011; PRISMA-P 2015)

Cochrane Collaboration; formalized through PROSPERO and PRISMA-P initiatives

A protocol-based meta-analysis is a meta-analysis conducted according to a detailed, pre-registered protocol that specifies all key methodological decisions — research questions, eligibility criteria, search strategy, outcome measures, and statistical methods — before data collection begins. Pre-registration, typically through PROSPERO or a comparable registry, distinguishes this approach from post-hoc or exploratory meta-analyses and substantially reduces the risk of selective reporting and outcome switching.

Evidence synthesis with pre-registered protocol

Aggregated quantitative data from primary studies (effect sizes, sample sizes, outcomes)

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane.

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1.

systematic-literature-review meta-analysis prisma-based-review prisma-compliant-meta-analysis scoping-review network-meta-analysis


Protocol-based Meta-ethnography

pre-registered meta-ethnography
prospero meta-ethnography
registered qualitative synthesis
protocol-driven meta-ethnography

1988 (meta-ethnography); protocol-based practice formalised 2010s

Noblit & Hare (meta-ethnography); protocol registration formalised through PROSPERO and eMERGe guidance

Protocol-based meta-ethnography is a structured qualitative evidence synthesis that follows Noblit and Hare's meta-ethnography method while requiring a pre-registered, publicly available protocol — typically on PROSPERO — before the review is conducted. Pre-registration constrains post-hoc decision-making, enhances methodological transparency, and aligns qualitative synthesis with the rigour standards now expected by leading journals and funders.

Qualitative evidence synthesis with pre-registered protocol

Published qualitative studies (text); protocol registration forms

Noblit, G. W., & Hare, R. D. (1988). Meta-Ethnography: Synthesizing Qualitative Studies. Sage.

France, E. F., Cunningham, M., Ring, N., Uny, I., Duncan, E. A. S., Jepson, R. G., Maxwell, M., Roberts, R. J., Turley, R. L., Booth, A., Britten, N., Flemming, K., Garside, R., Hannes, K., Lewin, S., Noblit, G. W., Pope, C., Thomas, J., Toye, F., Cargo, M., & Britten, N. (2019). Improving reporting of meta-ethnography: The eMERGe reporting guidance. BMC Medical Research Methodology, 19(1), 25.

meta-ethnography qualitative-meta-synthesis systematic-literature-review scoping-review prisma-based-review narrative-synthesis


Protocol-based Systematic literature review

Protocol-Registered Systematic Literature Review

protocol-registered SLR
pre-registered systematic review
PROSPERO-registered systematic review
protocol-driven systematic review

1990s–2015 (Cochrane Handbook 1st ed. 1994; PRISMA-P 2015)

Cochrane Collaboration; Moher et al. (PRISMA-P)

A protocol-based systematic literature review is a systematic review conducted according to a fully pre-specified and publicly registered research protocol. By committing the review question, eligibility criteria, search strategy, and planned analyses to a registered document before data collection begins, this approach minimises post-hoc decision-making, selective outcome reporting, and the accumulation bias that can undermine the credibility of unregistered reviews. Registration platforms such as PROSPERO and the Open Science Framework provide permanent, time-stamped records of the protocol.

Evidence synthesis method with pre-specified protocol

Published studies, trial registries, grey literature (bibliographic records)

Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane. Retrieved from https://training.cochrane.org/handbook

systematic-literature-review prisma-based-review scoping-review meta-analysis umbrella-review rapid-review


Protocol-based Umbrella review

Protocol-based Umbrella Review of Systematic Reviews

pre-registered umbrella review
prospero-registered umbrella review
registered overview of reviews
protocol-driven umbrella review

2011-2015 (PROSPERO launched 2011; JBI umbrella review guidelines 2015)

Developed from umbrella review methodology; protocol registration practice formalized through PROSPERO (York) and JBI

A protocol-based umbrella review is an umbrella review — a synthesis of existing systematic reviews and meta-analyses on a common topic — conducted under a publicly pre-registered protocol, typically in PROSPERO or a similar registry. Pre-registering the protocol before data collection begins commits the research team to prospectively defined eligibility criteria, search strategy, appraisal tools, and synthesis methods, sharply reducing the risk of outcome reporting bias and post-hoc analytical flexibility.

Published systematic reviews and meta-analyses

Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review. JBI Evidence Implementation, 13(3), 132-140.

Ioannidis, J. P. A. (2009). Integration of evidence from multiple meta-analyses: a primer on umbrella reviews, treatment networks and multiple treatments meta-analyses. CMAJ, 181(8), 488-493.

umbrella-review systematic-literature-review scoping-review network-meta-analysis prisma-compliant-umbrella-review meta-analysis


Prototype Theory

Prototype Theory is a framework for understanding how humans categorize concepts, proposing that categories are organized around prototypes—the most typical or central members. Developed by Eleanor Rosch in 1973, the theory challenges classical logic's view that categories have fixed boundaries defined by necessary-and-sufficient features. Instead, prototypes have fuzzy boundaries and graded membership: some instances are more central (robin is a prototypical bird) while others are peripheral (penguin is a bird but less typical). Prototype Theory has profound implications for understanding language, cognition, and meaning.

Rosch, E. (1973). Natural categories. Cognitive Psychology, 4(3), 328-350.

Lakoff, G. (1987). Women, Fire, and Dangerous Things: What Categories Reveal About the Mind. Chicago: University of Chicago Press.

Taylor, J. R. (2003). Linguistic Categorization: Prototypes in Linguistic Theory (3rd ed.). Oxford: Oxford University Press.


PROXIMITY-WLC

PROXIMITY-WLC (Proximity-Adjusted WLC — spatially explicit weighted linear combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Rinner, C.; Heppleston, A. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Spatially heterogeneous additive utility — criterion weights vary per alternative as a function of proximity to a reference location

Rinner, C., Heppleston, A. (2006). The spatial dimensions of multi-criteria evaluation — case study of a home buyer's spatial decision support system. Lecture Notes in Computer Science (GIScience 2006)

ahp anp bwm critic entropy swara fucom merec


Pruning Response Analysis

Morphological and Physiological Assessment of Pruning Effects on Growth and Productivity

pruning evaluation
shoot response measurement
canopy architecture assessment

Pruning response analysis systematically measures the morphological and physiological effects of pruning on fruit trees, including shoot development, branching architecture, flowering, fruit set, and yield. By combining visual assessment with growth measurements and phenological tracking, growers and researchers can quantify the effectiveness of pruning strategies and optimize training systems. This method is foundational to modern precision horticulture and cultivar-specific orchard management.

López-Balduz, S., Buesa, I., Perera-Fernández, L. G., & Carbonell, T. (2006). Physiological drop in citrus trees: Flowering phenology, fruit set, and cropping patterns. Journal of the American Society for Horticultural Science, 131(5), 589–599.

Bangerth, F. (1994). Response of apple trees to pruning: Vigour, flowering, yield and fruit quality. Acta Horticulturae, 386, 137–147.

crop-load-management phenological-stage-monitoring plant-propagation-success grafting-success-evaluation


Pruritus VAS

1983 (VAS); widely adopted for pruritus 1990s–2000s

Huskisson EC (VAS methodology); adapted for pruritus in dermatology

The Pruritus Visual Analog Scale (VAS) is a simple, single-item patient-administered tool measuring itch intensity on a continuous 0–10 (or 0–100) scale. Adapted from the original VAS for pain, it is one of the most frequently used outcome measures in dermatological research and clinical practice due to its simplicity, brevity, and responsiveness to treatment. Pruritus VAS is essential in trials of antipruritic therapies and in conditions where itching is a primary complaint (atopic dermatitis, psoriasis, pruritus ani, lichen planus).

Elman S, Hynan LS, Gabriel V, Mayo MJ. The 5-D itch scale: a new measure of pruritus. Br J Dermatol. 2010;162(3):587-593.

Huskisson EC. Visual analog scales. In: Melzack R, ed. Pain Measurement and Assessment. New York: Raven Press; 1983:33-37.


Psoriatic Arthritis Quality of Life Scale

The PsAQoL is a disease-specific patient-reported outcome measure of quality of life impact in psoriatic arthritis (PsA), a chronic inflammatory condition affecting joints and skin. Developed by McKenna and Doherty in 1997, PsAQoL comprises 20 items assessing the multidimensional impact of PsA on physical function, emotional well-being, work productivity, and social participation. PsAQoL captures the patient's lived experience of the disease, complementing clinical disease activity measures (CRP, joint counts) and providing a holistic view of treatment benefit in PsA research and practice.

Soderlin MK, Bergman S. Psychometric properties of the Psoriatic Arthritis Quality of Life (PsAQoL) instrument: Rasch analysis. Arthritis Care Research. 2011;63(11):1589-1595.

McKenna F, Doherty M. PsAQoL: a quality of life instrument for psoriatic arthritis. Clin Exp Rheumatol. 1997;15(6):630-634.

basdai basfi das28 sdai-rheumatoid-arthritis sledai


Pseudoflow

Pseudoflow Algorithm for Maximum Weighted Closure

The Pseudoflow Algorithm, developed by Dorit Hochbaum in 1992, is a polynomial-time algorithm for computing maximum weighted closures in directed acyclic graphs. In mining, it solves the ultimate pit limit problem more efficiently than earlier methods. By maintaining feasible pseudoflows and iteratively eliminating negative-cost nodes, it achieves near-optimal practical performance even on industrial-scale block models.

Efficient algorithm for maximum closure problem

Hochbaum, D. S. (1992). A new-old algorithm for minimum-cut and maximum-flow problems. Journal of the ACM, 1(1), 76-109.

Hochbaum, D. S. (2001). A fast algorithms for mining and metallurgical pits optimization. SIAM Journal on Computing, 30(4), 1096-1117.


PSI

PSI (Preference Selection Index) is a ranking multi-criteria decision-making (MCDM) method introduced by Maniya, K., Bhatt, M. G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Preference variation index (weight-free statistical)

Maniya, K., Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design

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Psychoacoustic Masking

Psychoacoustic Masking Models for Audio Perception

masking
temporal masking
frequency masking
auditory masking

Psychoacoustic masking describes how the human auditory system suppresses the perception of weak sounds in the presence of stronger sounds. Formalized by Eberhard Zwicker in the 1960s, masking is a fundamental phenomenon in hearing and the basis for perceptual audio coding (MP3, AAC, OPUS). Masking occurs both in frequency (spectral masking) and time (temporal masking), and understanding these effects enables efficient audio compression and realistic sound design.

Zwicker, E., & Scharf, B. (1965). Psychoacoustics: Facts and Models. Springer-Verlag.

Moore, B. C. J. (2012). An Introduction to the Psychology of Hearing (6th ed.). Academic Press.

Johnston, J. D. (1988). Transform coding of audio signals using perceptual noise criteria. IEEE Journal on Selected Areas in Communications, 6(2), 314–323.

speech-intelligibility fxlms-active-noise-control bark-and-mel-scales linear-predictive-coding cepstral-analysis


Psycholinguistic Eye-Tracking

Eye-Tracking in Psycholinguistic Research

Eye Gaze Tracking
Reading Behavior Analysis

Psycholinguistic Eye-Tracking is a method that measures eye movements during reading or visual processing to investigate how the mind processes language. Pioneered by Keith Rayner, eye-tracking reveals which parts of text attract attention, how long readers spend on different words, and how eye movements relate to comprehension difficulties. Metrics like fixation duration and saccade length provide objective, millisecond-level data on cognitive processing. Eye-tracking is now a standard tool for studying reading, comprehension, and attention.

Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422.

Rayner, K. (Ed.). (2012). The Oxford Handbook of Eye Movements. Oxford: Oxford University Press.

Duchowski, A. T. (2007). Eye Tracking Methodology: Theory and Practice (2nd ed.). London: Springer.


Psychological Capital Questionnaire

The Psychological Capital Questionnaire (PCQ-24) measures psychological capital—the positive psychological resources of efficacy, hope, resilience, and optimism—that enable individuals to thrive in demanding work environments. Developed by Luthans, Youssef, and Avolio in 2007, it operationalizes a positive organizational psychology framework that complements traditional competency assessments. PsyCap scores predict engagement, performance, well-being, and retention.

Luthans, F., Youssef, C. M., & Avolio, B. J. (2007). Psychological capital and beyond. Oxford University Press.

Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007). Positive psychological capital: Measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541–572.

Avey, J. B., Luthans, F., & Jensen, S. M. (2009). Psychological capital: A positive resource for combating employee stress and turnover. Human Resource Management, 48(5), 677–693.

core-self-evaluations-scale proactive-personality-scale perceived-organizational-support career-adapt-abilities-scale leader-member-exchange-scale


Psychological Safety Scale

Psychological Safety Scale (PSS) - Team-Level Measure

The Psychological Safety Scale (PSS), developed by Amy Edmondson in 1999, measures team members' shared perception that they can take interpersonal risks—speaking up, asking questions, admitting mistakes, proposing new ideas—without fear of embarrassment, punishment, or rejection. The 7-item scale captures a team-level construct fundamental to learning, innovation, and psychological well-being. High psychological safety predicts team performance, learning from errors, information sharing, and adaptive responses to change.

Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383.

Edmondson, A. C. (2018). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. Hoboken, NJ: Wiley.

organizational-justice-scale transformational-leadership-scale servant-leadership-scale job-demands-resources-scale organizational-commitment-scale


PCL-SV

PCL-SV
Hare PCL-SV
Psychopathy Checklist Screening

Stephen D. Hart, Diana N. Cox, Robert D. Hare

The Psychopathy Checklist Screening Version (PCL-SV) is a 12-item assessment tool developed by Hart, Cox, and Hare (1995) to screen for psychopathic personality traits in adolescents and adults. It is a brief alternative to the full 20-item Psychopathy Checklist-Revised (PCL-R), designed for rapid screening in correctional, forensic psychiatric, and research settings. PCL-SV identifies individuals exhibiting callousness, impulsivity, shallow affect, and antisocial behavior—traits associated with high violence risk and treatment non-responsiveness.

Hart, S. D., Cox, D. N., & Hare, R. D. (1995). The Hare Psychopathy Checklist: Screening Version (PCL-SV). Multi-Health Systems, Inc.

Hare, R. D. (1991). The Hare Psychopathy Checklist-Revised. Multi-Health Systems, Inc.

hcr-20 violence-risk-appraisal-guide novaco-anger-scale level-of-service-inventory