Polysomnography

Polysomnography (PSG) is a comprehensive multi-channel physiological recording method that simultaneously records brain electrical activity, eye movements, muscle tone, respiratory effort, oxygen saturation, heart rate, and limb movements during sleep. First systematized by Rechtschaffen and Kales in 1968, polysomnography is the gold standard for diagnosing sleep disorders, characterizing sleep architecture, and assessing the quality and organization of sleep in humans and increasingly in veterinary species.

Rechtschaffen, A., & Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages in Human Subjects. National Institutes of Health Publication.

Iber, C., Ancoli-Israel, S., Chesson, A. L., & Quan, S. F. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (1st ed.). American Academy of Sleep Medicine.

Mitchell, E. K., & Redlin, U. (2011). Polysomnography and actigraphy in laboratory animals. Sleep, 34(11), 1431-1432.


Polytomous Confirmatory Factor Analysis

CFA for ordered categories
ordinal CFA
categorical CFA
WLSMV-CFA

Polytomous confirmatory factor analysis (CFA) tests a pre-specified factor structure when items have three or more ordered response categories (e.g., Likert scales). By working with polychoric correlations and robust estimators such as WLSMV, it avoids the distortions that arise when ordered categorical data are treated as continuous.

Latent variable / confirmatory measurement model

Polytomous (ordered categorical) item responses

Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.

Muthen, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.

confirmatory-factor-analysis polytomous-item-response-theory ordinal-confirmatory-factor-analysis measurement-invariance polytomous-exploratory-factor-analysis item-response-theory


Polytomous Construct Validity

Polytomous Construct Validity Assessment

polytomous item construct validity
ordered-category construct validity
polytomous measurement validity
multi-category scale validity

Building on Messick (1989) and IRT extensions by Masters, Muraki, and Samejima

Polytomous construct validity refers to the evaluation of whether a scale composed of ordered, multi-category items (e.g., Likert or rating-scale items) genuinely measures the intended latent construct. It extends classical validity frameworks to polytomous measurement models — such as the Graded Response Model or Generalized Partial Credit Model — ensuring that ordered response categories function as designed and that the resulting scores reflect the target construct.

Ordinal polytomous items (Likert scales, rating scales, partial-credit tasks)

Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176.

Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.

confirmatory-factor-analysis partial-credit-model graded-response-model polytomous-rasch-model differential-item-functioning exploratory-factor-analysis


Polytomous DIF

Polytomous Differential Item Functioning

Polytomous DIF
DIF for polytomous items
ordinal DIF analysis
graded-response DIF

Bruno D. Zumbo and colleagues (ordinal logistic regression framework); Robert D. Ankenmann, Hariharan Swaminathan and others (IRT-based extensions)

Polytomous differential item functioning detects whether a test or survey item with more than two ordered response categories (e.g., Likert-type scales, partial-credit items) functions differently across groups such as gender, ethnicity, or language background, after controlling for the latent trait being measured. It extends classical binary DIF methods to ordinal response formats.

Measurement fairness / item bias detection

Ordinal polytomous item scores (Likert scales, partial-credit tests)

Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Directorate of Human Resources Research and Evaluation, Department of National Defense.

Osterlind, S. J. & Everson, H. T. (2009). Differential Item Functioning (2nd ed.). SAGE Publications.

mantel-haenszel-dif logistic-regression-dif item-response-theory graded-response-model confirmatory-factor-analysis measurement-invariance


Polytomous EFA

EFA for ordered-categorical data
polychoric EFA
ordinal exploratory factor analysis
polytomous factor analysis

Polytomous exploratory factor analysis extends standard EFA to ordered categorical (Likert-type) response data by replacing the Pearson correlation matrix with a polychoric correlation matrix. It recovers the latent continuous variable that each polytomous item is assumed to reflect, yielding more accurate factor loadings and better-defined factor structures than treating ordinal scores as if they were continuous.

Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.

Muthén, B. (1978). Contributions to factor analysis of dichotomous variables. Psychometrika, 43(4), 551–560.

exploratory-factor-analysis confirmatory-factor-analysis polychoric-correlation item-response-theory ordinal-alpha graded-response-model


Polytomous item analysis

ordered-category item analysis
graded response analysis
polytomous IRT
rated-scale item analysis

Fumiko Samejima (graded response model, 1969); David Andrich (rating scale model, 1978); Geoffrey Masters (partial credit model, 1982)

Polytomous item analysis examines the psychometric behavior of items that have more than two ordered response categories — such as Likert-type scales or partial-credit tasks. It evaluates each item's difficulty thresholds, discriminating power, and category functioning to determine whether the full response scale is being used as intended and whether each item contributes reliably to measuring the underlying construct.

Ordinal polytomous responses (e.g., Likert-scale items)

Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34(4, Pt. 2), 1–97.

Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.

graded-response-model partial-credit-model rating-scale-model confirmatory-factor-analysis classical-item-analysis exploratory-factor-analysis


Polytomous McDonald's omega

McDonald's Omega Reliability for Polytomous Items

ordinal omega
omega for polytomous items
categorical omega
omega polychoric

1999 (omega); 2007 (polytomous extension)

Roderick P. McDonald (omega); extension for polytomous items by Zumbo, Gadermann & Zeisser

Polytomous McDonald's omega estimates the internal consistency reliability of a scale composed of ordinal (polytomous) items — such as Likert-type responses — by computing omega from a factor model fitted to the polychoric correlation matrix rather than the Pearson correlation matrix, yielding estimates that are unbiased by the discreteness of item responses.

Polytomous ordinal items (Likert-type, rating scales)

Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta as measures of internal consistency for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.

McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.

mcdonalds-omega cronbachs-alpha ordinal-reliability-analysis polytomous-item-response-theory confirmatory-factor-analysis ordinal-cronbachs-alpha


Polytomous Measurement Invariance

Polytomous Measurement Invariance Testing

polytomous multi-group measurement invariance

Polytomous measurement invariance testing evaluates whether a scale with ordered categorical (polytomous) response options — such as Likert-type items — measures the same latent construct in the same way across two or more groups. It extends classical multi-group CFA invariance testing to properly account for the ordinal nature of item responses, ensuring that group comparisons of latent means or factor structures are substantively valid.

Millsap, R. E. & Kwok, O.-M. (2004). Evaluating the impact of partial factor loading and intercept invariance on selection utility. Psychological Methods, 9(2), 200–215.

Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.


Polytomous Rasch Model

PRM
Rating Scale Model
Partial Credit Model
Polytomous IRT Rasch

Gerhard N. Masters (Partial Credit Model); David Andrich (Rating Scale Model)

The Polytomous Rasch Model extends the dichotomous Rasch framework to ordered response scales with three or more categories, such as Likert items or partial-credit tasks. It estimates person ability and item difficulty on the same interval-level logit scale, and it tests whether the response categories function as intended — prerequisites for rigorous ordinal measurement.

Ordered polytomous response categories (Likert, rating scales, partial-credit scored items)

Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174.

Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561–573.

item-response-theory rasch-model ordinal-item-response-theory confirmatory-factor-analysis differential-item-functioning measurement-invariance


Polytomous Reliability Analysis

Reliability Analysis for Polytomous Items

polytomous scale reliability
ordinal reliability estimation
reliability for ordered-category items
polychoric reliability analysis

2007–2009 (formal ordinal extensions); broader framework since 1950s

Building on Cronbach (1951) and McDonald (1978); ordinal extensions by Zumbo and colleagues (2007) and Green and Yang (2009)

Polytomous reliability analysis estimates the internal consistency or precision of measurement for scales composed of items with more than two ordered response categories, such as Likert-type, rating, or partial-credit items. It corrects a well-known underestimation bias in conventional Cronbach's alpha by working with polychoric correlations or IRT-based precision indices.

Polytomous (ordered multi-category) item responses

Green, S. B. & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74(1), 155–167.

Zumbo, B. D., Gadermann, A. M. & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.

cronbachs-alpha mcdonalds-omega polytomous-item-response-theory ordinal-reliability-analysis confirmatory-factor-analysis item-response-theory


Polytomous scale development

polytomous item development
ordered-category scale construction
rating scale development
multi-category item development

Samejima, F.; Masters, G. N. (independently)

Polytomous scale development is the systematic construction and validation of measurement instruments whose items have three or more ordered response categories — such as Likert-type, rating, or partial-credit items. It applies polytomous item response theory models or ordinal factor analysis methods to evaluate item quality, estimate latent trait levels, and build a psychometrically sound scale.

Ordered polytomous item responses (rating scales, Likert-type)

item-response-theory ordinal-scale-development confirmatory-factor-analysis differential-item-functioning scale-development generalizability-theory


Pontryagin Maximum Principle

The Pontryagin Maximum Principle (PMP) is a fundamental theorem in optimal control theory providing necessary conditions for optimality of a control trajectory. Published by Lev Pontryagin in 1962, PMP generalizes the calculus of variations to control problems with constraints and is the theoretical foundation enabling solution of complex trajectory optimization problems from spacecraft missions to industrial process optimization.

Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., & Mischenko, E. F. (1962). The Mathematical Theory of Optimal Processes. John Wiley & Sons.


Pooled Mean Group (PMG)

PMG Estimator
Pooled Mean Group
PMG Panel Estimator
Havuzlanmış Ortalama Grup Tahmincisi

The Pooled Mean Group (PMG) estimator, introduced by Pesaran, Shin, and Smith (1999), is a panel data technique designed for dynamic heterogeneous panels where the long-run equilibrium relationship is common across groups but short-run dynamics and error variances are allowed to differ. It is particularly suited for macro-panels with moderate N and T, such as cross-country growth, energy consumption, and financial development studies.

Heterogeneous short-run, homogeneous long-run

Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634.


Pooled OLS

Pooled OLS
Pooled Ordinary Least Squares
Simple Panel OLS
Havuzlanmış EKK

Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity.

Linear regression on stacked panel observations

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.


Population Pharmacodynamics

PopPD
population PD
hierarchical PD modeling

Population pharmacodynamic (PopPD) modeling integrates pharmacokinetics with individual dose-response relationships across patient populations to characterize drug efficacy and tolerability. Pioneered by Lewis Sheiner and colleagues, PopPD accounts for inter-individual variability in drug effects and enables rational dose optimization and response prediction.

Dahlström, B., & Nyberg, L. (1993). Population pharmacokinetics and pharmacodynamics. Clinical Pharmacokinetics, 24(1), 45-57.

Sheiner, L. B., & Steimer, J. L. (1992). Pharmacokinetic/pharmacodynamic modeling in drug development. Annual Review of Pharmacology and Toxicology, 40, 67-95.


Population Pharmacokinetics

Population Pharmacokinetics (Nonlinear Mixed-Effects)

PopPK
Nonlinear Mixed-Effects Modeling
NONMEM Approach
Popülasyon Farmakokinetiği

Population Pharmacokinetics (PopPK) is a nonlinear mixed-effects modeling framework that characterizes how drugs are absorbed, distributed, metabolized, and eliminated across a patient population, estimating both typical population parameters and the magnitude of between-subject variability. Introduced by Sheiner, Rosenberg, and Marathe in 1977, it enables parameter estimation from sparse, routinely collected clinical data—making it indispensable in drug development, regulatory submissions, and individualized dosing.

Nonlinear mixed-effects regression model

Sheiner, L. B., Rosenberg, B., & Marathe, V. V. (1977). Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. Journal of Pharmacokinetics and Biopharmaceutics, 5(5), 445–479.


Population Viability Analysis

PVA
extinction risk
minimum viable population
MVP

Population Viability Analysis (PVA), introduced by Shaffer (1981), estimates the probability that a population will persist over a given time period under specified conditions. PVA combines demographic models (Leslie matrices, IPMs) with stochastic simulation to project population trajectories, quantifying extinction risk. This allows conservation planners to assess whether a population will likely persist, evaluate management scenarios, and estimate the minimum viable population (MVP) size for long-term persistence. PVA is a decision-support tool, not a precise predictor.

Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134.

Morris, W. F., Blakesley, D., Bruna, M. E., et al. (2002). A practical handbook for population viability analysis. NatureServe, Arlington, Virginia.

Ralls, K., Ballou, J. D., & Templeton, A. R. (1988). Estimates of lethal equivalents and the cost of inbreeding in mammals. Conservation Biology, 2(2), 185-193.

leslie-matrix integral-projection-model life-table-response-experiment metabolic-theory-of-ecology


Populism Scale

PAS
Akkerman Populism Scale
Populist Attitudes Measure

Matthijs Bukkerman, Cas Mudde, Andrej Zaslaysky

The Populism Attitudes Scale measures individual propensity toward populist political orientations, including Manichean worldview (pure people vs. corrupt elites), belief in popular sovereignty, and anti-elitism. Developed by Akkerman, Mudde, and Zaslaysky (2014), the eight-item scale distinguishes populist attitudes from left-right ideology, authoritarian attitudes, and distrust of institutions. It captures voters' susceptibility to populist political messaging across left-wing and right-wing populist movements globally, from Latin American left-populism to European right-wing populism.

Akkerman, A., Mudde, C., & Zaslaysky, A. (2014). How populist are the people? Measuring populist attitudes in voters. Comparative Political Studies, 47(9), 1324-1353.

Mudde, C. (2004). The populist zeitgeist. Government and Opposition, 39(4), 541-563.

Canovan, M. (1999). The people. Cambridge: Polity Press.


Mean-Variance Portfolio Optimization

Markowitz Mean-Variance Portfolio Optimization

Ortalama-Varyans Portföy Optimizasyonu (Markowitz)

Mean-variance portfolio optimization is the foundational model of modern portfolio theory, introduced by Harry Markowitz in 1952. It describes portfolios in an expected-return versus risk (variance) plane and traces the efficient frontier of allocations that offer the highest expected return for each level of risk, covering the minimum-variance portfolio, the maximum-Sharpe-ratio portfolio, and constrained variants.

Maximise expected return for a given variance (or minimise variance for a given return)

Output: Portfolio weights on the efficient frontier

Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.

Ledoit, O. & Wolf, M. (2004). A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices. Journal of Multivariate Analysis, 88(2), 365-411.

risk-parity-model credit-risk-models interest-rate-models backtesting-var arima


POS Tagging

part-of-speech tagging
grammatical tagging
Sözcük Türü Etiketleme (POS Tagging)

Part-of-speech tagging assigns a grammatical category label — noun, verb, adjective, and so on — to every word in a text. It is a foundational natural-language-processing task, formalised as a statistical model by Ratnaparkhi (1996) and packaged into widely used toolkits such as Stanford CoreNLP (Manning et al., 2014), and it serves as a preliminary step for syntactic analysis and information extraction.

Output: One grammatical category tag per token (noun, verb, adjective, ...)

Preliminary step for syntactic parsing and information extraction

Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP.

Manning, C.D. et al. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. ACL.


Positive Mental Health Scale

Multiple developers including Christine Lüthy

The Positive Mental Health Scale (PMHS) is a brief instrument developed to measure mental well-being by assessing the presence of positive mental health dimensions rather than the absence of disorder. Rather than focusing solely on symptom reduction, the PMHS operationalizes mental health as an active state characterized by personal strengths, resilience, coping capacity, and positive functioning. It represents a paradigm shift toward strength-based mental health assessment, viewing mental health and mental illness as distinct continua rather than opposite ends of a single spectrum.

Lüthy, C., Meisser, C., & Schindler, C. (2015). The Positive Mental Health Scale: A measure based on personal strength models in a cross-national study. Health and Quality of Life Outcomes, 13, 29.

flourishing-scale perma-scale who-5-wellbeing-index subjective-wellbeing-scale


Possibility Theory

Fuzzy Possibility Theory
Possibilistic Reasoning
Olasılık Teorisi (Bulanık)
Possibility Distribution Theory

Lotfi Zadeh; Didier Dubois & Henri Prade

Possibility Theory is a mathematical framework for representing and reasoning under uncertainty, introduced by Lotfi Zadeh in 1978 and systematically developed by Didier Dubois and Henri Prade in their 1988 monograph. It uses possibility distributions — functions assigning a degree in [0,1] to each element of a universe — to encode what is plausible or consistent with available information, complementing probability theory for situations where data is scarce or knowledge is imprecise.

Possibility distribution over a universe of discourse

Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press.

Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.


Post-Deployment Reintegration Scale

Post-Deployment Reintegration Difficulties Scale

Sayer, Noorbaloochi, Frazier, & colleagues

The Post-Deployment Reintegration Scale measures multidimensional adjustment difficulties experienced by service members transitioning from military to civilian life. Developed by Sayer, Noorbaloochi, and colleagues in 2010, it assesses challenges across employment, family relationships, social reintegration, identity development, and health domains. It is widely used in VA clinical settings, military transition programs, and research examining post-deployment adjustment outcomes and predictors of successful reintegration.

Mobbs, M. C., Bonanno, G. A., & Bonanno, M. L. (2006). Beyond the myth of resilience: A prospective study of resilience and adjustment following military separation. Psychological Trauma: Theory, Research, Practice, and Policy, 2(1), 68-82.

Sayer, N. A., Noorbaloochi, S., Frazier, P., Carlson, K., Gravely, A., & Murdoch, M. (2010). Reintegration problems and treatment interests among Iraq and Afghanistan combat veterans receiving VA medical care. Psychiatric Services, 61(6), 589-597.

military-identity-scale deployment-risk-resilience soldier-adaptation-measure military-to-civilian-transition


Post-Occupancy Evaluation

Post-Occupancy Evaluation of Building Performance

POE
building performance evaluation
occupant satisfaction assessment

Building performance and occupant assessment

Post-Occupancy Evaluation (POE) is a systematic method for assessing how well a completed building meets the needs and expectations of its occupants, comparing planned performance to actual performance. Formalized by Wolfgang Preiser in the 1980s, POE has become essential for learning what design strategies work, identifying problems for remediation, and improving future projects.

Preiser, W. F., Rabinowitz, H. Z., White, E. T. (1988). Post-Occupancy Evaluation. Van Nostrand Reinhold, New York.

Leaman, A., Stevenson, F. (2010). Evaluating Operation and Use of the Building. Building Research and Information, 38(3), 287-301.

Baird, G. (2010). Sustainable Building in Practice: What the Users Think. Routledge, London.


Post-Quantum Cryptography (Kyber)

PQC
quantum-resistant cryptography
quantum-safe

Post-quantum cryptography comprises cryptographic algorithms believed to be secure against both classical and quantum computers. In 2022, NIST standardized post-quantum algorithms including ML-KEM (CRYSTALS-Kyber) for key encapsulation and ML-DSA (CRYSTALS-Dilithium) for signatures. Post-quantum cryptography is essential for systems requiring long-term confidentiality, as adversaries may record encrypted communications today and decrypt them once quantum computers become available.

post-quantum key encapsulation mechanism

Avanzi, R., Bos, J., Ducas, L., & Kiltz, E. (2022). CRYSTALS-Kyber algorithm specification and supporting documentation. NIST Post-Quantum Cryptography Project.

National Institute of Standards and Technology (NIST). (2016). Post-Quantum Cryptography: Call for Proposals. NIST Special Publication 800-56C Rev. 1.


Post-Traumatic Growth Inventory

Positive psychological outcomes and resilience following trauma

Richard G. Tedeschi & Lawrence G. Calhoun

The PTGI is a 21-item self-report scale measuring positive psychological outcomes and personal growth reported after trauma exposure. Developed by Tedeschi and Calhoun in 1996, the PTGI operationalizes the construct of posttraumatic growth (PTG)—the experience of positive life change accompanying psychological struggle with trauma. Unlike scales measuring psychopathology or symptom severity, the PTGI captures meaningful psychological and existential shifts often reported by trauma survivors, including enhanced relationships, increased personal strength, spiritual change, and life appreciation.

Tedeschi, R. G., & Calhoun, L. G. (1996). The Post-Traumatic Growth Inventory: Measuring the positive legacy of trauma. Journal of Traumatic Stress, 9(3), 455-471.

Tedeschi, R. G., & Calhoun, L. G. (2004). Posttraumatic growth: Conceptual foundations and empirical evidence. Psychological Inquiry, 15(1), 1-18.


Postcolonial Analysis

postcolonial criticism
postcolonial theory
colonial discourse analysis
decolonial analysis

Late 20th century (Said 1978; Spivak 1988; Bhabha 1994)

Edward Said, Gayatri Chakravorty Spivak, Homi K. Bhabha

Postcolonial analysis is a qualitative research approach that critically examines the lasting cultural, political, epistemic, and social effects of colonialism and imperialism. Drawing on foundational works by Edward Said, Gayatri Spivak, and Homi Bhabha, it interrogates how colonial power relations are reproduced in texts, institutions, identities, and knowledge systems — and how colonised or marginalised voices can be recovered, amplified, and centred.

Texts, documents, interviews, cultural artefacts, media, historical records

Purposive selection of documents or participants; no fixed number

Said, E. W. (1978). Orientalism. Pantheon Books.

Spivak, G. C. (1988). Can the subaltern speak? In C. Nelson & L. Grossberg (Eds.), Marxism and the Interpretation of Culture (pp. 271–313). University of Illinois Press.

discourse-analysis narrative-analysis ethnography thematic-analysis content-analysis action-research


Postharvest Storage Simulation

Computational Storage Life Prediction and Quality Modeling

shelf life prediction
storage modeling
quality decay simulation

Postharvest storage simulation uses computational models to predict fruit and vegetable quality degradation during storage and distribution under variable temperature and humidity conditions. Pioneered by Tijskens and Nicolaï in 2001, these mechanistic and empirical models enable logistics optimization, reduce food waste, and improve supply chain transparency. They are integrated into decision support systems for commercial packinghouses and research facilities.

Tijskens, L. M., & Polderdijk, J. J. (2001). A generic model for keeping quality of vegetable produce during storage and distribution. Postharvest Biology and Technology, 23(1), 13–25.

Hertog, M. L. A. T. M., Nicolaï, B. M., & Tijskens, L. M. (2007). Modelling the effect of storage conditions on the quality of postharvest horticultural produce: a review. Postharvest Biology and Technology, 45(3), 309–320.

cold-storage-protocol controlled-atmosphere-storage ripeness-index brix-measurement


Postpartum Bonding Questionnaire

The Postpartum Bonding Questionnaire (PBQ) is a 25-item self-report instrument designed to assess disorders of the mother-infant relationship and bonding in the early postpartum period. Developed by Brockington and colleagues in 2001, the PBQ screens for pathological bonding experiences, including impaired attachment, rejection, resentment, and anger toward the infant. A shorter 16-item version (PBQ-16) is also available for rapid screening in clinical settings.

Brockington, I. F., Oates, J., George, S., Turner, D., Vostanis, P., Sullivan, M., Loh, C., & Murdoch, C. (2001). A screening questionnaire for mother-infant bonding disorders. Archives of Women's Mental Health, 3(4), 133-140.

Brockington, I. F., Aucamp, H. M., & Fraser, C. (2006). Severe disorders of the mother-infant relationship: sorting out the concepts. Archives of Women's Mental Health, 9(3), 129-138.


Potential Vorticity Inversion

PV inversion
Potential vorticity
PV thinking

Potential vorticity (PV) inversion is a diagnostic technique that reconstructs atmospheric wind and pressure fields from the spatial distribution of potential vorticity. This method assumes that, in a geostrophically balanced atmosphere, the PV field uniquely determines the balanced circulation around anomalies.

Haynes, P., & McIntyre, M. E. (1987). On the evolution of vorticity and potential vorticity in the atmosphere. Journal of the Atmospheric Sciences, 44(5), 828-841.

Davis, C. A. (1992). Piecewise potential vorticity inversion. Journal of the Atmospheric Sciences, 49(19), 1853-1862.


Potentiometric Titration

Potentiometric titration is an electrochemical method of analysis that measures the potential difference between a reference electrode and an indicator electrode as a titrant is gradually added to a solution. Developed in the early 20th century, it allows precise determination of the concentration of analytes without requiring visual endpoint indicators. This method is fundamental in analytical chemistry for determining acids, bases, redox species, and metal ions.

Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.

Covington, A. K., Bates, R. G., & Durst, R. A. (1985). Definitions of pH scales, standard reference values, measurement of pH and related terminology. Pure and Applied Chemistry, 57(3), 531–542.

Michels, H. H., & Sielcken, O. E. (1965). A potentiometric method for the determination of moisture. Journal of Applied Chemistry, 15(12), 589–594.

ion-chromatography coulometry voltammetry uv-vis-spectrophotometry atomic-absorption-spectroscopy


Power Analysis for ANOVA

Statistical Power Analysis for Analysis of Variance

ANOVA power analysis
F-test power analysis
sample size for ANOVA
Güç Analizi — ANOVA

Power analysis for ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs.

effect size f, alpha, desired power, number of groups k

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

one-way-anova power-analysis-ttest power-analysis-regression independent-t-test effect-size-f


Chi-Square Power Analysis

Statistical Power Analysis for Chi-Square Tests

chi-square power
chi-square sample size
Ki-Kare Güç Analizi
goodness-of-fit power
independence test power

Chi-square power analysis is a prospective calculation that determines the minimum sample size required — or the statistical power achievable with a given sample — for chi-square independence tests or goodness-of-fit tests. It rests on Cohen's w effect size framework, codified by Jacob Cohen in his landmark 1988 work on statistical power for the behavioral sciences.

chi-square-independence-test chi-square-goodness-of-fit power-analysis-t-test power-analysis-anova power-analysis-correlation fisher-exact-test


Correlation Power Analysis

Statistical Power Analysis for Pearson Correlation

Korelasyon Güç Analizi
power analysis for r
sample size for correlation

Correlation power analysis is a pre-study calculation that determines how many participants are needed — or how much statistical power an existing sample provides — for a Pearson correlation test. Formalised by Jacob Cohen in his landmark 1988 text, it uses the expected correlation coefficient r directly as the effect size, so researchers can plan studies that are neither underpowered nor wastefully large.

pearson-correlation power-analysis-t-test power-analysis-anova power-analysis-regression spearman-correlation


Multilevel Power Analysis

Power Analysis for Multilevel and Mixed-Effects Models

HLM power analysis
mixed-effects power analysis
clustered design power analysis
Çok Düzeyli / Karma Model Güç Analizi

Snijders & Bosker; Hox, Moerbeek & van de Schoot

Multilevel power analysis is a sample-size planning procedure designed for hierarchical, clustered, or longitudinal study designs in which observations are nested within higher-level units such as students within schools or patients within clinics. Formalized in the multilevel modeling literature by Snijders and Bosker (1993, expanded 2012) and Hox, Moerbeek, and van de Schoot (2017), it accounts for the intraclass correlation (ICC) and the design effect that arises when data are clustered, ensuring that both the number of clusters and the cluster size are adequate to detect a target effect.

Sample-size planning for hierarchical designs

ICC, number of clusters (J), cluster size (m), effect size

cross-sectional clustered, longitudinal, panel

Snijders, T.A.B. & Bosker, R.J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). SAGE.

Hox, J.J., Moerbeek, M. & van de Schoot, R. (2017). Multilevel Analysis: Techniques and Applications (3rd ed.). Routledge.

power-analysis-anova power-analysis-regression hierarchical-linear-modeling mixed-effects-model intraclass-correlation one-way-anova


Power Analysis for Proportions

Sample Size and Power Analysis for Proportion Tests

proportion power analysis
two-proportion z-test power
z-test for proportions power
Oran Testi Güç Analizi

Power analysis for proportion tests is a prospective sample-size planning method used to determine how many participants are needed to detect a meaningful difference between two (or one) proportions with a specified probability. Formalised by Jacob Cohen in his 1988 landmark text, it applies the arcsine transformation to convert proportions into the effect-size index h, enabling direct calculation of the required sample size.

power-analysis-t-test power-analysis-anova chi-square-test two-proportion-z-test fisher-exact-test binomial-test


Power Analysis for Regression

A Priori Power Analysis for Multiple Regression

regression power analysis
sample size estimation regression
f² power analysis
Güç Analizi — Regresyon

Power analysis for multiple regression is a pre-study procedure, formalised by Jacob Cohen (1988), that calculates the minimum sample size needed to detect a regression effect of a given size with adequate statistical power. It uses the anticipated R² (or the equivalent Cohen's f² effect size) and the number of predictors to determine how many observations must be collected before data collection begins.

anticipated R², or f², number of predictors (u), α level, desired power (1−β)

Green, S. B. (1991). How Many Subjects Does It Take To Do A Regression Analysis? Multivariate Behavioral Research, 26(3), 499–510.

power-analysis-ttest power-analysis-anova power-analysis-correlation multiple-linear-regression sample-size-determination


SEM Power Analysis

Power Analysis for Structural Equation Modeling and Multivariate Analyses

SEM sample size planning
covariance structure power analysis
MANOVA power analysis
SEM / Çok Değişkenli Güç Analizi

Power analysis for SEM and other multivariate procedures determines the minimum sample size required to detect a model misfit of a specified magnitude with adequate probability. The dominant approach, introduced by MacCallum, Browne, and Sugawara in 1996, expresses effect size as the Root Mean Square Error of Approximation (RMSEA) and derives power from the noncentral chi-square distribution.

Sample size planning (multivariate / SEM)

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.

power-analysis-anova power-analysis-regression power-analysis-multilevel simulation-based-power structural-equation-modeling manova


Survival Analysis Power Analysis

Sample Size and Power Analysis for Survival Analysis (Log-rank and Cox Regression)

log-rank power analysis
cox regression power analysis
survival power analysis
Sağkalım Analizi Güç Analizi

Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981) and Lachin (1981) and remain the standard approach in clinical trial planning.

Sample size determination for survival outcomes

log-rank test, Cox proportional hazards regression

Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319.

Lachin, J. M. (1981). Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials, 2(2), 93–113.

log-rank-test cox-proportional-hazards kaplan-meier power-analysis-ttest power-analysis-proportion simulation-based-power


Power Analysis for t-test

Statistical Power Analysis for the t-test

t-test power analysis
sample size calculation for t-test
Güç Analizi — t-Testi

Power analysis for the t-test is a sample size planning procedure that determines how many participants are required to detect a mean difference of a given magnitude with acceptable probability. Formalised by Jacob Cohen in his 1969 and 1988 editions of Statistical Power Analysis for the Behavioral Sciences, it links four quantities — effect size (Cohen's d), significance level (α), statistical power (1 − β), and sample size — so that fixing any three allows calculation of the fourth.

independent-t-test paired-t-test welch-t-test power-analysis-anova power-analysis-regression


Power analysis

sample size calculation
power calculation
sensitivity analysis
a priori power analysis

Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.

Continuous, categorical, or count outcomes depending on target test

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.

effect-size-analysis independent-samples-t-test one-way-anova chi-square-test sample-size-determination bayesian-power-analysis


Power Flow Analysis

Power Flow Analysis and Load Flow Computation

Power flow analysis, also called load flow study, is a computational method that determines the steady-state voltage, current, and power distribution across all buses in an electrical power system. Developed by Ward and Hale in 1956, it is fundamental to power system planning, operation, and optimization.

Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.

Grainger, J. J., & Stevenson, W. D. (1994). Power System Analysis and Design (3rd ed.). McGraw-Hill.

Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2014). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.

fault-analysis-power-system smart-grid-state-estimation reactive-power-compensation load-forecasting harmonic-distortion-analysis


POWER-MEAN

POWER-MEAN (Weighted Power Mean (Hölder Mean)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Hardy, G. H. Littlewood, J. E. Pólya, G. in 1934. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Power mean family — parametric generalisation of WAM/WGM/WHM

Hardy, G. H., Littlewood, J. E., Pólya, G. (1934). Inequalities. Cambridge University Press


Power Quality Assessment

Power Quality Measurement and Assessment

PQ assessment
power quality survey
voltage quality analysis

Power quality assessment evaluates the suitability of electrical voltage and current waveforms for reliable equipment operation. It measures deviations from ideal sinusoidal waveforms, including voltage sags, swells, harmonics, transients, and imbalance. Comprehensive assessment is critical for ensuring equipment protection, identifying root causes of malfunctions, and optimizing mitigation strategies.

IEEE Std 1159-2019: IEEE Recommended Practice for Monitoring Electric Power Quality.

IEC 61000-2-2:2002: Electromagnetic compatibility (EMC) - Part 2-2: Environment - Compatibility levels for low-frequency conducted disturbances and signalling in public low-voltage power supply systems.

Dugan, R. C., McGranaghan, M. F., Santoso, S., & Beaty, H. W. (2012). Electrical Power Systems Quality (3rd ed.). McGraw-Hill.

harmonic-distortion-analysis power-flow-analysis load-forecasting smart-grid-state-estimation


Power Spectral Density Estimation

Power Spectral Density (PSD) Estimation Methods

PSD Estimation
Spectral Density Analysis
Power Spectrum Estimation

Power Spectral Density (PSD) estimation is a set of methods for determining how the power of a signal is distributed across different frequencies. Proposed by Peter Welch in 1967, PSD estimation techniques are fundamental to frequency domain signal analysis, providing insights into the frequency composition of signals for applications ranging from communications to biomedical monitoring.

Welch, P. (1967). The Use of Fast Fourier Transform for Estimation of Power Spectra: A Method Based on Time Averaging over Short, Modified Periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73.

Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.

short-time-fourier-transform wiener-filter matched-filter adaptive-lms-filter


Power System State Estimation

Power System State Estimation via Weighted Least Squares

PSSE
WLS State Estimation
Power Flow State Estimation

Power System State Estimation (PSSE) is a real-time algorithm that estimates the voltage and phase angle at every bus in a power grid from a set of noisy, redundant measurements. Introduced by Schweppe in 1970, it combines measurements (power flows, voltage magnitudes) with the physical power flow model to produce the most likely system state. State estimation is the foundation of modern grid control centers, providing operators with an accurate digital representation of the actual network.

Real-time state estimation using measurements and physical models

Schweppe, F. C., & Wildes, J. (1970). Power system static-state estimation: III system implementation. IEEE Transactions on Power Apparatus and Systems, 89(1), 120-125.

Abur, A., & Expósito, A. G. (2004). Power System State Estimation: Theory and Implementation. Marcel Dekker.

Primadianto, A., & Lu, C. N. (2017). A review of distribution system state estimation. IEEE Transactions on Power Systems, 32(5), 3859-3869.


PPI Network Topology

Protein-Protein Interaction Network Analysis and Topology

protein interaction networks
interactome analysis
network topology

Protein-protein interaction network analysis identifies and characterizes the structural properties of cellular interaction networks. Pioneered by Uetz and colleagues through large-scale yeast two-hybrid screening, this approach reveals topological features like hubs, modules, and motifs that encode functional organization and disease associations.

Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., ... & Lomax, J. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), 623-627.

Barabási, A. L. & Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2), 101-113.

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., ... & Mering, C. V. (2021). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 49(D1), D605-D612.


Pragmatic AB Design

Pragmatic AB Single-Case Experimental Design

pragmatic single-case AB design
real-world AB design
AB phase design
naturalistic AB design

1968 (AB single-case design); pragmatic framing formalized ~2000s–2010s

Rooted in applied behavior analysis (Baer, Wolf, Risley, 1968); pragmatic framing developed across clinical and educational single-case research traditions

The Pragmatic AB Design is a single-case experimental design that collects repeated measurements of one individual or unit across two consecutive phases: a baseline phase (A) with no intervention, followed by an intervention phase (B). Deployed in real-world, clinically feasible conditions rather than tightly controlled laboratory settings, it is widely used in behavioral health, rehabilitation, education, and applied psychology to generate actionable evidence about individual-level treatment effects.

Repeated measures of a single participant or unit across baseline (A) and intervention (B) phases

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

Tate, R. L., Perdices, M., Rosenkoetter, U., McDonald, S., Togher, L., Shadish, W., Horner, R., Kratochwill, T., Barlow, D. H., Kazdin, A., Sampson, M., Shamseer, L., & Vohra, S. (2016). The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement. Physical Therapy, 96(7), e1–e10.

ab-design aba-design abab-design multiple-baseline-design pragmatic-trial single-case-experimental-design


Pragmatic A/B Test

Pragmatic A/B Testing (Pragmatic Randomized Experiment)

pragmatic split test
real-world A/B experiment
pragmatic online experiment
pragmatic controlled experiment

1967 (pragmatic framing); 2007–2012 (large-scale tech A/B testing practice)

Pragmatic trial framing: Schwartz & Lellouch (1967); A/B testing in technology: Ron Kohavi and colleagues at Microsoft (~2007–2012)

A pragmatic A/B test is a randomized comparative experiment that evaluates two alternatives — a control (A) and a treatment (B) — under real-world operating conditions rather than tightly controlled laboratory settings. Rooted in the pragmatic-versus-explanatory trial distinction introduced by Schwartz and Lellouch in 1967 and brought to large-scale practice by online experimentation teams at Microsoft, Google, and Amazon, it prioritizes decision-relevant effectiveness over internal mechanistic explanation.

Metric outcomes (click rates, conversion rates, revenue, task completion, clinical endpoints)

Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.

Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.

ab-testing randomized-controlled-trial pragmatic-trial factorial-design multivariate-testing quasi-experimental-design


Pragmatic ABA Design

Pragmatic ABA Reversal Single-Subject Experimental Design

pragmatic reversal design
naturalistic ABA design
real-world ABA reversal design
pragmatic withdrawal design

1968 (ABA base); pragmatic adaptation in applied behavioral research from 1970s onward

ABA reversal design: Baer, Wolf & Risley (1968); pragmatic orientation: Schwartz & Lellouch (1967)

The Pragmatic ABA Design is a single-subject reversal experiment conducted under real-world, naturalistic conditions rather than tightly controlled laboratory settings. It follows the classic baseline (A1) — intervention (B) — withdrawal/return-to-baseline (A2) sequence while deliberately relaxing control conditions to reflect authentic practice environments. This approach prioritizes external validity and clinical utility, making findings directly applicable to schools, clinics, and community settings.

Single-subject experimental design with pragmatic orientation

Repeated behavioral observations (continuous or interval data) collected in natural settings

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

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


Pragmatic ABAB design

pragmatic reversal design
pragmatic withdrawal design
applied ABAB design
pragmatic single-case reversal

1968 (classic ABAB); pragmatic adaptations formalised 1990s–2000s

Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed in applied behavior analysis and clinical psychology literature

The pragmatic ABAB design is a single-case experimental design that adapts the classic reversal (ABAB) logic to real-world clinical and applied constraints. It alternates between a baseline phase (A) and an intervention phase (B) twice, demonstrating experimental control through repeated phase changes while allowing flexibility — such as abbreviated withdrawals or partial reversals — when full withdrawal of treatment is ethically or practically impossible.

Repeated measures (behavioral or clinical outcomes) over time

Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.

abab-reversal-design multiple-baseline-design single-case-experimental-design interrupted-time-series changing-criterion-design alternating-treatments-design


Pragmatic adaptive experiment

pragmatic adaptive trial
real-world adaptive trial
PAT
adaptive pragmatic RCT

Synthesized from pragmatic trial tradition (Schwartz & Lellouch, 1967) and adaptive design methodology; formalized convergence in 2000s–2010s

A pragmatic adaptive experiment is a hybrid clinical trial design that combines the real-world generalizability of pragmatic trials with the statistical flexibility of adaptive designs. It enrolls a broad, representative patient population under routine care conditions, while using pre-specified interim analyses to modify trial parameters — such as sample size, allocation ratios, or arm selection — as outcome data accumulate. The result is a design that is both externally valid and resource-efficient.

Clinical outcomes, electronic health records, patient-reported outcomes

Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Sydes, M. R. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

adaptive-trial-design randomized-controlled-trial platform-trial stepped-wedge-design cluster-randomized-trial bayesian-adaptive-design


Pragmatic case-control study

real-world case-control study
pragmatic case-control design
effectiveness case-control study
PCCS

1950s–1960s (classical); pragmatic framing 1967–2000s

Evolved from classical case-control methodology (Dorn, 1954; Cornfield, 1956); pragmatic framing formalized by Schwartz & Lellouch (1967)

A pragmatic case-control study is an observational design that compares individuals who have developed a disease or outcome (cases) with those who have not (controls), using data collected under routine real-world conditions rather than strictly controlled experimental settings. Exposure histories are reconstructed from clinical records, registries, or administrative databases. The design is chosen when a conventional explanatory case-control study would be impractical, unethical, or too narrow to inform actual clinical or public-health decisions.

Observational epidemiological study design

Routine clinical records, administrative databases, registry data, electronic health records

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

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., Tunis, S., Bergel, E., Harvey, I., Magid, D. J., & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.

case-control-study nested-case-control pragmatic-randomized-clinical-trial retrospective-case-control-study matched-case-control-study cohort-study


Pragmatic case series

real-world case series
pragmatic observational case series
practice-based case series

Pragmatic framing formalized 1967; case series practice predates 20th century

Pragmatic framework: Schwartz & Lellouch (1967); case series design: longstanding clinical tradition

A pragmatic case series is an observational study that documents consecutive or purposively selected patients receiving a clinical intervention or presenting with a condition under routine, real-world practice conditions — without randomization, a control group, or the highly controlled eligibility criteria characteristic of explanatory trials. It is used to describe treatment patterns, outcomes, and adverse events as they occur in everyday clinical settings.

Clinical records, patient-reported outcomes, routinely collected real-world data

van Walraven, C., & Davis, D. A. (2007). Case series and case report. In: Knottnerus JA, Buntinx F (eds). The Evidence Base of Clinical Diagnosis. 2nd ed. BMJ Books.

case-series pragmatic-randomized-clinical-trial prospective-case-series retrospective-case-series cohort-study case-report


Pragmatic Clinical Trial

Pragmatic Randomized Controlled Trial (Pragmatic RCT)

pragmatic trial
real-world trial
effectiveness trial
PRECIS-2

Thorpe et al. (2009); PRECIS framework developed by international consortia

A pragmatic trial is designed to evaluate the real-world effectiveness of an intervention in typical clinical settings with diverse, representative patients, minimal exclusion criteria, and clinically relevant outcomes. Developed by Thorpe and colleagues (2009) and formalized via the PRECIS-2 framework (2015), pragmatic trials bridge the gap between explanatory efficacy trials (conducted in controlled research settings) and implementation science, answering the question 'Does this work in actual clinical practice?' rather than 'Can this work under ideal conditions?'

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Tugwell, P. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. CMAJ, 180(10), E47–E57.

Loudon, K., Treweek, S., Sullivan, F., Donnan, P., Thorpe, K. E., & Zwarenstein, M. (2015). The PRECIS-2 tool: designing trials that are fit for purpose. BMJ, 350, h2147.

Glasziou, P., Altman, D. G., Bossuyt, P., Boutron, I., Clarke, M., Julious, S., ... & Moher, D. (2018). Reducing waste from incomplete or unusable reports of biomedical research. The Lancet, 383(9913), 267–276.

randomized-controlled-trial real-world-evidence cluster-randomized-trial implementation-science adaptive-trial-design


Pragmatic control group experimental design

Pragmatic Control Group Experimental Design

pragmatic controlled trial
effectiveness trial with control group
real-world control group design
pragmatic comparative design

1967 (seminal distinction); 2009 (PRECIS operationalization)

Schwartz & Lellouch (pragmatic vs explanatory distinction); extended by PRECIS framework (Thorpe et al.)

A pragmatic control group experimental design tests whether an intervention works under routine, real-world conditions by comparing it against a control condition — typically usual care or an active comparator — rather than a tightly controlled placebo. It prioritises external validity and applicability over the internal purity of an explanatory efficacy trial, asking whether an intervention makes a meaningful difference to people as they are actually treated in practice.

Continuous, categorical, or ordinal outcome measures from real-world participants


Pragmatic Cross-Sectional Epidemiological Study

pragmatic cross-sectional survey
real-world cross-sectional study
observational cross-sectional study
prevalence survey

Mid-20th century onwards; pragmatic framing prominent from 1967

Classical epidemiology tradition; pragmatic framing refined by Schwartz & Lellouch (1967) and subsequent real-world evidence literature

A pragmatic cross-sectional epidemiological study measures the prevalence of exposures, outcomes, and risk factors in a defined population at a single point in time, conducted under real-world conditions rather than tightly controlled experimental settings. It provides a snapshot of the health status of a community or patient group, making it one of the most widely used designs for surveillance, needs assessment, and hypothesis generation in clinical and public-health epidemiology.

Cross-sectional survey data, administrative records, clinical registry data

cohort-study case-control-study ecological-study cluster-sampling systematic-review prevalence-estimation


Pragmatic diagnostic accuracy study

real-world diagnostic accuracy study
pragmatic DAS
routine-care diagnostic study
pragmatic test evaluation

2000s–2010s (formalized alongside STARD reporting guidelines)

Evolved from STARD initiative (Bossuyt et al.) and pragmatic trial framework (Schwartz & Lellouch, 1967)

A pragmatic diagnostic accuracy study evaluates how well a diagnostic test performs under real-world clinical conditions — not in idealized, tightly controlled settings. Conducted within routine care workflows, it measures sensitivity, specificity, predictive values, and likelihood ratios for an index test against a reference standard, yielding accuracy estimates directly applicable to clinical practice rather than laboratory benchmarks.

Patient-level clinical data from routine care settings; index test and reference standard results

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

Schilling, I., & Burchardt, M. (2018). Pragmatic diagnostic accuracy studies: bridging the gap between explanatory trials and routine practice. Diagnostic and Prognostic Research, 2(1), 14.

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


Pragmatic Dose-Response Analysis

Pragmatic Dose-Response Analysis in Epidemiology

real-world dose-response analysis
pragmatic exposure-response study
dose-response in pragmatic trials
effectiveness dose-response analysis

1990s–2000s (formalized in pragmatic trial context)

Rooted in pharmacoepidemiology and pragmatic trial methodology; PRECIS framework by Thorpe et al. (2009)

Pragmatic dose-response analysis quantifies how varying levels of an exposure or treatment relate to clinical outcomes under real-world conditions. By embedding dose-response questions within pragmatic study designs — broad eligibility criteria, routine care settings, and heterogeneous populations — it bridges the gap between controlled pharmacological dose-finding and the messy variability of everyday clinical practice. The approach is especially valued when the goal is to establish or refine optimal dosing guidance from evidence that reflects actual patient populations.

Observational or experimental quantitative method

Continuous or ordinal exposure measurements, clinical outcomes, real-world patient records

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.

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Schulz, K. F. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

dose-response-analysis pragmatic-randomized-clinical-trial cohort-study pragmatic-cohort-study survival-analysis regression-discontinuity-design


Pragmatic ecological study

real-world ecological study
effectiveness ecological study
population-level pragmatic study
pragmatic ecologic design

1967–1982 (pragmatic concept 1967; ecological study formalized ~1982)

Morgenstern (ecological study framework); Schwartz & Lellouch (pragmatic design concept)

A pragmatic ecological study is an observational epidemiological design that examines associations between exposures and outcomes at the population or group level — using routinely collected, real-world data — with the explicit goal of informing practical public health decisions under everyday conditions. Rather than controlling every variable in a laboratory-like manner, it embraces the complexity and heterogeneity of natural settings to answer effectiveness questions relevant to policy.

Observational ecological study with pragmatic framing

Aggregate population-level data (routinely collected, administrative, or surveillance data)

Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344.

Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.

ecological-study pragmatic-cohort-study pragmatic-randomized-clinical-trial cross-sectional-epidemiological-study cohort-study dose-response-analysis


Pragmatic Factorial Experiment

pragmatic factorial trial
pragmatic factorial RCT
real-world factorial design
PFE

Synthesized from pragmatic trial methodology (Schwartz & Lellouch, 1967) and factorial design principles (Fisher, 1935); formalized in clinical research contexts in the 2000s–2010s

A pragmatic factorial experiment combines two powerful methodological frameworks: the factorial experimental design — which tests multiple intervention components simultaneously — and the pragmatic trial orientation, which prioritizes real-world applicability, broad eligibility criteria, and flexible delivery conditions. The result is a design that efficiently evaluates which components of a complex intervention work, and whether they interact, while maintaining ecological validity for health, behavioral, and educational research.

Quantitative outcome data; continuous, binary, or count outcomes from real-world participants

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): New Methods for More Potent eHealth Interventions. American Journal of Preventive Medicine, 32(5 Suppl), S112–S118.

factorial-design randomized-controlled-trial pragmatic-trial multiphase-optimization-strategy cluster-randomized-trial stepped-wedge-design


Pragmatic Field Experiment

pragmatic effectiveness trial
real-world field experiment
effectiveness field trial
practical field study

1967 (pragmatic framing); 2009 (PRECIS tool)

Schwartz & Lellouch (pragmatic framing); formalized for practice through PRECIS framework (Thorpe et al.)

A pragmatic field experiment tests whether an intervention works under real-world, routine conditions rather than under the tightly controlled settings of a laboratory or explanatory trial. It combines the pragmatic trial philosophy — prioritising external validity and decision-relevance — with field experimentation, so findings directly inform policy and practice. The design is positioned toward the pragmatic end of the PRECIS continuum and is widely used in public health, education, agriculture, and behavioral economics.

Continuous, binary, or count outcome data collected in natural field settings

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., Tunis, S., Bergel, E., Harvey, I., Magid, D. J., & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

field-experiment pragmatic-randomized-controlled-trial natural-experiment cluster-randomized-field-experiment adaptive-field-experiment randomized-controlled-trial


Pragmatic Fractional Factorial Experiment

pragmatic FFE
fractional factorial trial
pragmatic factorial design
FFD in pragmatic settings

Fractional factorial designs: 1940s–1950s; pragmatic application: 2000s–2010s

Building on Fisher (1935); pragmatic adaptation by Collins, Murphy & Strecher (2007) via MOST framework

A pragmatic fractional factorial experiment applies fractional factorial design principles to real-world or clinical intervention research, enabling simultaneous evaluation of multiple intervention components in a resource-efficient fraction of the full factorial runs. Popularised through the Multiphase Optimization Strategy (MOST), it identifies which components of a multi-component intervention contribute meaningfully to outcomes before a confirmatory randomized trial is conducted.

Continuous, binary, or count outcome data from randomized participants

Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5S), S112–S118.

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


Pragmatic Full Factorial Experiment

Pragmatic Full Factorial Experimental Design

pragmatic factorial trial
real-world full factorial design
effectiveness full factorial experiment
pragmatic 2^k experiment

1920s (factorial); 1967/2009 (pragmatic framework)

Full factorial: R.A. Fisher (1920s); Pragmatic framing: Schwartz & Lellouch (1967), formalized by Thorpe et al. (2009)

A pragmatic full factorial experiment combines the complete crossing of all factor levels (the full factorial structure) with the broad eligibility criteria, flexible delivery, and real-world conditions of a pragmatic trial. Every possible combination of factors is tested simultaneously, yielding both main effects and all interaction effects, while deliberately relaxing strict laboratory controls to reflect how interventions actually operate in practice.

Continuous, ordinal, or binary outcome data from real-world settings

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalmers, I. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.


Pragmatic Kaplan-Meier analysis

Pragmatic Kaplan-Meier Survival Analysis

pragmatic KM analysis
real-world Kaplan-Meier
pragmatic survival curve estimation
KM analysis in pragmatic trials

1958 (estimator); pragmatic application formalized 1967 onward

Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967)

Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a treatment effect under ideal conditions, pragmatic designs accept real-world heterogeneity, and the resulting survival curves reflect the effectiveness of an intervention as it actually performs in clinical practice.

Non-parametric survival estimator within pragmatic study design

Time-to-event data with censoring from real-world or pragmatic clinical settings

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

kaplan-meier-analysis pragmatic-randomized-clinical-trial survival-analysis cox-proportional-hazards competing-risks-analysis log-rank-test


Pragmatic Laboratory Experiment

pragmatic experiment
applied laboratory trial
practice-oriented lab experiment
pragmatic controlled experiment

1967 (foundational distinction); 2009 (PRECIS operationalization)

Schwartz & Lellouch (pragmatic–explanatory distinction); extended by PRECIS framework developers

A pragmatic laboratory experiment is a controlled study conducted in a laboratory setting that prioritises external validity and real-world applicability over the stringent internal controls characteristic of purely explanatory experiments. Drawing on the pragmatic–explanatory continuum formalised by Schwartz and Lellouch (1967) and later operationalised in the PRECIS framework, it asks whether an intervention works under conditions that approximate actual practice rather than ideal circumstances, making findings directly actionable for decision-makers and practitioners.

Experimental design philosophy and study type

Quantitative outcome data collected under real-world or near-real-world laboratory conditions

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

randomized-controlled-trial factorial-design quasi-experimental-design explanatory-trial field-experiment pilot-study


Pragmatic Mixed Methods Design

pragmatic MMR
pragmatism-guided mixed methods
pragmatic inquiry design
practical mixed methods

Early 2000s (formalised); pragmatism as philosophy late 19th–early 20th century

John W. Creswell & Vicki L. Plano Clark (formalised); philosophical grounding in William James, John Dewey, Richard Rorty

Pragmatic mixed methods design is a research approach that selects and combines quantitative and qualitative methods based on what best answers the research question, rather than adhering to a single philosophical paradigm. Rooted in the philosophical tradition of pragmatism — associated with William James, John Dewey, and later Richard Rorty — it treats methodological fit and practical utility as the primary criteria for design decisions. The approach is endorsed by leading mixed methods scholars including Creswell and Plano Clark as the most common philosophical worldview underpinning mixed methods work.

Quantitative data, qualitative data, or both collected in any sequence

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

Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1(1), 48–76.


Pragmatic Multi-Arm Experiment

Pragmatic Multi-Arm Randomized Experiment

pragmatic multi-arm trial
multi-arm pragmatic RCT
pragmatic multi-treatment experiment
PMAT

1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s

Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research

A pragmatic multi-arm experiment is an experimental design that simultaneously compares three or more interventions (arms) under real-world conditions rather than tightly controlled laboratory settings. It combines the broad eligibility, flexible delivery, and effectiveness orientation of pragmatic trials with the statistical efficiency of multi-arm structures, allowing researchers to evaluate multiple treatments or treatment variants against each other or a control within a single study, minimizing the resources and time required relative to running separate pairwise trials.

Continuous, categorical, or binary outcome data collected under routine real-world conditions

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.

Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Clinical Epidemiology, 20(8), 637-648.

pragmatic-randomized-controlled-trial multi-arm-experiment adaptive-multi-arm-experiment cluster-randomized-multi-arm-experiment factorial-multi-arm-experiment pragmatic-adaptive-experiment


Pragmatic Multiple Baseline Design

PMBD
pragmatic MBD
real-world multiple baseline design
flexible multiple baseline design

1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s

Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed within single-case methodology community

The Pragmatic Multiple Baseline Design is a single-case experimental design that staggers intervention introduction across multiple participants, settings, or behaviors in real-world conditions where strict experimental control is impractical. By relaxing some idealized constraints — such as perfectly stable baselines or rigid staggering timelines — it preserves the core logic of the multiple baseline while accommodating clinical, educational, or community realities. It is especially valued when withholding treatment for ethical reasons is untenable and when practitioners need evidence from naturalistic settings.

Repeated-measures behavioral or outcome data across participants, settings, or behaviors

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

Shadish, W. R., & Sullivan, K. J. (2011). Characteristics of single-case designs used to assess intervention effects in 2008. Behavior Research Methods, 43(4), 971–980.

multiple-baseline-design single-case-experimental-design reversal-design randomized-controlled-trial stepped-wedge-design interrupted-time-series


Pragmatic nested case-control

real-world nested case-control
pragmatic NCC
nested case-control in routine data
real-world evidence nested case-control

1977 (nested case-control); pragmatic variant emerged in real-world evidence research from 1990s onwards

Duncan Thomas (nested case-control); pragmatic design concept from Schwartz & Lellouch (1967)

A pragmatic nested case-control study embeds a case-control analysis within a pre-existing real-world cohort — typically drawn from electronic health records, administrative claims, or disease registries — to examine associations between exposures and outcomes under routine clinical conditions. Controls are sampled from the risk set (those still at risk at the time each case occurs), preserving temporal sequence while dramatically reducing data-collection costs compared with a full cohort analysis.

Routine healthcare data, electronic health records, administrative databases, registry data

Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.

Schneeweiss, S., & Avorn, J. (2005). A review of uses of health care utilization databases for epidemiologic research on therapeutics. Journal of Clinical Epidemiology, 58(4), 323–337.

nested-case-control case-control-study cohort-study pragmatic-cohort-study pragmatic-randomized-clinical-trial retrospective-nested-case-control


Pragmatic phase II clinical trial

pragmatic Phase II trial
real-world Phase II trial
Phase II pragmatic RCT
Phase IIb pragmatic trial

Pragmatic framework: 1967; Phase II application: 1990s–2000s

Conceptual basis: Daniel Schwartz & Joseph Lellouch (pragmatic vs. explanatory distinction, 1967); applied to Phase II context by drug developers and trialists from the 1990s onward

A pragmatic Phase II clinical trial is an early-to-mid-stage interventional study that evaluates a new treatment's preliminary efficacy and safety under conditions that approximate real-world clinical practice rather than tightly controlled experimental settings. It sits between pure explanatory Phase II trials and large pragmatic Phase III confirmatory trials, prioritising practical feasibility and clinical relevance while still generating the signal needed to justify further development.

Clinical outcomes, patient-reported outcomes, routinely collected health data, biomarker data

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

phase-ii-clinical-trial pragmatic-randomized-clinical-trial adaptive-phase-ii-clinical-trial pragmatic-phase-iii-clinical-trial phase-i-clinical-trial randomized-clinical-trial


Pragmatic phase III clinical trial

Pragmatic Phase III Randomized Controlled Trial

pragmatic RCT
effectiveness trial
real-world RCT
pragmatic trial

1967 (Schwartz & Lellouch); formalized further in 2000s–2010s

Schwartz & Lellouch (distinction between pragmatic and explanatory trials)

A pragmatic phase III clinical trial is a large-scale randomized study designed to evaluate whether an intervention works under the conditions of everyday clinical practice rather than the tightly controlled environment of an explanatory efficacy trial. It recruits a broad, representative patient population, allows flexibility in treatment delivery, and measures outcomes that matter to patients and health systems, generating evidence directly applicable to real-world treatment decisions.

Patient-level clinical outcome data from routine or near-routine care settings

Ford, I., & Norrie, J. (2016). Pragmatic trials. New England Journal of Medicine, 375(5), 454–463.

randomized-controlled-trial explanatory-clinical-trial cluster-randomized-trial adaptive-clinical-trial intention-to-treat-analysis real-world-evidence-study


Pragmatic phase IV study

pragmatic post-marketing study
real-world phase IV trial
pragmatic pharmacovigilance study
pragmatic post-approval study

1967 (pragmatic concept); 2000s (pragmatic Phase IV formalized)

Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); PRECIS framework by Thorpe et al. (2009)

A pragmatic Phase IV study is a post-marketing investigation conducted under routine clinical conditions to evaluate a drug or device's real-world effectiveness, long-term safety, and comparative performance. Unlike the controlled Phase III environment, it intentionally minimizes protocol restrictions — broad eligibility criteria, standard-of-care comparators, and naturalistic follow-up — to generate evidence directly applicable to everyday clinical practice.

Observational / interventional hybrid study design

Routine clinical records, patient registries, electronic health records, survey data

Atkinson, M. J., & Lennox, R. D. (2012). Planning pragmatic clinical trials in Phase IV: pragmatic versus explanatory studies in post-marketing evaluation. Contemporary Clinical Trials, 33(2), 213-218.

phase-iv-study pragmatic-randomized-clinical-trial cohort-study pragmatic-cohort-study dose-response-analysis screening-test-evaluation


Pragmatic pretest-posttest experimental design

Pragmatic Pretest-Posttest Experimental Design

pragmatic pre-post design
real-world pretest-posttest study
effectiveness pre-post design
pragmatic before-after study

1963 (pre-post design); 1967 (pragmatic distinction)

Schwartz & Lellouch (pragmatic framing); Campbell & Stanley (pre-post design)

A pragmatic pretest-posttest experimental design combines the before-after measurement structure of the classic pre-post design with the real-world, high-external-validity ethos of pragmatic research. Participants are assessed on relevant outcomes before an intervention is delivered in routine or naturalistic conditions, then re-assessed afterward. The goal is to estimate the effectiveness of the intervention as it actually works in practice rather than under ideal, tightly controlled efficacy conditions.

Experimental / quasi-experimental design

Quantitative outcome measures collected at two time points (pre and post intervention)

Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637-648.

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


Pragmatic randomized clinical trial

pragmatic RCT
effectiveness trial
real-world RCT
practical clinical trial

A pragmatic randomized clinical trial (pragmatic RCT) is an interventional study that tests whether a treatment works under routine clinical conditions, as opposed to the tightly controlled setting of an explanatory trial. It prioritizes broad eligibility, flexible delivery, and patient-relevant outcomes to answer the question 'Does this treatment work in everyday practice?' rather than 'Can this treatment work under ideal circumstances?' The distinction between pragmatic and explanatory trials was formally articulated by Schwartz and Lellouch in 1967 and operationalized by the PRECIS tool in 2009.

Clinical outcomes, patient-reported outcomes, routine care records

Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.

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


Pragmatic Randomized Controlled Trial

pRCT
pragmatic trial
practical clinical trial
real-world RCT

A pragmatic randomized controlled trial (pRCT) tests whether an intervention works under ordinary, real-world conditions — broad eligibility, flexible delivery, and routine care settings. Participants are still randomly assigned to treatment or control, preserving causal inference, but the study is designed to reflect the diversity and variability of actual practice rather than the ideal conditions of an explanatory trial. The defining framework is the PRECIS-2 tool, which maps any RCT along nine pragmatic-to-explanatory dimensions.

Quantitative outcome data collected under routine or real-world care conditions

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., … Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

randomized-controlled-trial explanatory-randomized-controlled-trial cluster-randomized-controlled-trial adaptive-randomized-controlled-trial factorial-randomized-controlled-trial stepped-wedge-design


Pragmatic Screening Test Evaluation

Pragmatic Screening Test Evaluation Study

pragmatic diagnostic screen evaluation
real-world screening evaluation
effectiveness-oriented screening study
PRECIS-guided screening evaluation

2000s-2010s (formalized with PRECIS framework)

Pragmatic trial framework: Schwartz & Lellouch (1967); PRECIS tool: Thorpe et al. (2009)

A pragmatic screening test evaluation assesses the real-world effectiveness of a screening instrument under routine clinical or public-health conditions — rather than the tightly controlled, ideal-participant settings of explanatory studies. It asks whether the screening tool performs adequately in the actual populations and workflows where it will be deployed, prioritising external validity and implementation relevance over maximally controlled internal conditions.

Observational / quasi-experimental evaluation design

Routine clinical records, real-world patient data, screening test results, reference standard outcomes

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative. Annals of Internal Medicine, 138(1), 40-44.

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


Pragmatic Single-Subject Experimental Design

applied single-subject experimental design

1960s–1970s (SSED roots); pragmatic framing prominent from 1990s onward

Applied behavior analysis tradition (Sidman, Baer, Wolf, Risley); pragmatic adaptation from clinical research

Pragmatic single-subject experimental design applies the logic of single-case experimentation — repeated measurement, baseline comparison, and phase manipulation — within real-world practice settings rather than controlled laboratories. It allows practitioners and clinicians to rigorously evaluate interventions for individual participants without requiring large samples, making it especially valuable in applied, clinical, and educational contexts where heterogeneity across individuals is high.

Repeated measures on a single participant or unit (behavioral, clinical, or outcome data collected over time)

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

Tate, R. L., Perdices, M., Rosenkoetter, U., Shadish, W., Togher, L., Vohra, S., ... & Douglas, J. (2016). The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement. Archives of Scientific Psychology, 4(1), 1-9.

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


Pragmatic Solomon Four-Group Design

Pragmatic Solomon Four-Group Experimental Design

pragmatic S4GD
real-world Solomon four-group design
pragmatic pretest-control design
pragmatic Solomon design

1949 (Solomon design); pragmatic variant in applied use from 1990s onward

Solomon four-group design: Richard L. Solomon (1949); pragmatic orientation formalized by Schwartz & Lellouch (1967) and Thorpe et al. (2009)

The Pragmatic Solomon Four-Group Design combines the pretest-sensitization control logic of the classic Solomon (1949) four-group structure with the broad eligibility, flexible delivery, and real-world conditions characteristic of pragmatic trials. Four groups are formed: two receive the intervention (one pretested, one not) and two serve as controls (one pretested, one not), allowing simultaneous estimation of treatment effects and pretest sensitization effects under ecologically valid settings.

Continuous, ordinal, or binary outcome data; pretest and posttest measurements

Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.

Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.

solomon-four-group-design pragmatic-randomized-controlled-trial pretest-posttest-experimental-design control-group-experimental-design factorial-experimental-design cluster-randomized-controlled-trial


Pragmatic survival analysis

real-world survival analysis
pragmatic time-to-event analysis
effectiveness survival analysis
PSA

Conceptual framework: 1967; widespread application: 1990s–2000s

Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward

Pragmatic survival analysis applies time-to-event statistical methods within pragmatic or real-world settings, estimating how long patients survive, remain event-free, or retain treatment benefit under conditions of routine clinical practice. Unlike explanatory survival analyses conducted under tightly controlled trial conditions, the pragmatic variant embraces the heterogeneity, treatment switching, non-adherence, and competing events that characterise real-world patient populations, prioritising external validity over internal precision.

Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings

Time-to-event data with censoring; electronic health records, registry data, pragmatic trial data

Ford, I., & Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375(5), 454–463.

Royston, P., & Parmar, M. K. B. (2011). The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt. Statistics in Medicine, 30(19), 2409–2421.

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


Precision Agriculture with NDVI

Normalized Difference Vegetation Index Monitoring for Precision Crop Management

NDVI remote sensing
Vegetation index monitoring
Satellite crop monitoring

Precision Agriculture with NDVI is a geospatial monitoring pipeline for assessing crop vigor, health, and productivity using the Normalized Difference Vegetation Index (NDVI) derived from satellite or drone imagery. Developed by Rouse and colleagues (1973), this method enables rapid, non-destructive assessment of spatial variation in crop performance and informs variable-rate management decisions.

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite symposium, Washington, DC.

Thenkabail, P. S., Lyon, J. G., & Huete, A. (2018). Hyperspectral remote sensing of vegetation. CRC Press, Boca Raton, FL.

crop-growth-simulation nitrogen-use-efficiency weed-density-mapping crop-yield-estimation irrigation-scheduling-etref


Precision-Recall AUC

The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC.

Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.

Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10(3), e0118432.

precision recall f1-score roc-auc accuracy


Precision

Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.

recall f1-score accuracy specificity matthews-correlation-coefficient


Predatory Journals and Publishers

Identification and Avoidance of Predatory Journals and Publishers

Predatory Publishing
Fake Journals
Pay-to-Publish Schemes

Jeffrey Beall (University of Colorado Denver); international research community

Predatory journals are fake academic publishers that exploit the open-access model by charging authors publication fees without providing peer review, editorial oversight, or quality control. Coined by librarian Jeffrey Beall in 2010, the term describes publishers that prioritize profit over scientific integrity, accepting nearly all submissions (regardless of quality), using deceptive marketing (claiming high impact factors, faking indexing, using names similar to established journals), and often hosting work that would not survive peer review. Publishing in predatory journals damages an author's credibility and wastes research dissemination efforts.

Beall, J. (2010). Predatory Open-Access Scholarly Publishers. The Charleston Advisor, 11(4), 10–17.

Beall, J. (2015). Scholarly Open Access: Critical Analysis of Bibliometric Indicators and Journal Quality. PeerJ Preprints, 3, e1481.

Lak, A., Sarvari, S. A., Kassaian, N., & Salari, P. (2016). Identifying Predatory Journals. Asian Journal of Transfusion Science, 10(2), 184–185.

open-access-publishing peer-review-process cope-guidelines retraction-process


Predictive Site Location

Predictive site location modeling uses machine learning algorithms (particularly maximum entropy models) to predict the probability of archaeological site occurrence across a landscape based on environmental and spatial variables. Developed for ecology but adapted for archaeology, predictive modeling identifies areas with high archaeological potential, guiding survey strategies and resource management.

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.

Verhagen, P., & Whitley, T. W. (2012). Predictive modelling for archaeological resource management. Journal of Archaeological Science, 39(5), 1066-1077.


Pregnancy-Related Anxiety Questionnaire

Pregnancy-Related Anxiety Questionnaire-Revised 2 (PRAQ-R2)

The Pregnancy-Related Anxiety Questionnaire—Revised 2 (PRAQ-R2) is a 10-item self-report measure designed specifically to assess anxiety unique to pregnancy, focusing on two core pregnancy-related concerns: fear of bearing a handicapped child and worries about changes to one's own body and appearance. Developed and refined by Huizink and colleagues, the PRAQ-R2 is based on the observation that pregnancy-related anxiety reflects distinct cognitive and emotional preoccupations that differ from generalized anxiety disorders.

Huizink, A. C., Mulder, E. J., & Buitelaar, J. K. (2004). Prenatal stress and risk for psychopathology: specific effects or inert vulnerability? Psychological Bulletin, 130(1), 115-142.

Huizink, A. C., Robles de Medina, P. G., Mulder, E. J., Visser, G. H., & Buitelaar, J. K. (2016). Is pregnancy anxiety a distinctive syndrome? Early Human Development, 79(2), 81-91.


Premenstrual Symptoms Screening Tool

The Premenstrual Symptoms Screening Tool (PSST) is a brief, 19-item self-report questionnaire designed to screen for premenstrual dysphoric disorder (PMDD) and assess the severity of premenstrual symptoms. Developed by Steiner, Macdougall, and Brown in 2003, the PSST efficiently identifies women with clinically significant cyclical mood and physical symptoms warranting diagnostic evaluation and potential pharmacological or psychological treatment.

Steiner, M., Macdougall, M., & Brown, E. (2003). The Premenstrual Symptoms Screening Tool (PSST) for clinicians. Archives of Women's Mental Health, 6(3), 203-209.

Steiner, M., & Born, L. (2000). Diagnosis and treatment of premenstrual dysphoric disorder: an update. CNS Drug Reviews, 6(1), 5-17.


Preprint Servers in Science

Preprint Servers and Pre-Publication Dissemination

Preprints
Preprint Archives
Pre-publication Servers

Paul Ginsparg (arXiv, 1991); Cold Spring Harbor Laboratory (bioRxiv, 2013); NIH (medRxiv, 2019)

Preprint servers are open-access repositories where researchers post manuscripts before, during, or alongside peer review at a formal journal. Preprints allow rapid, free dissemination of research findings without waiting for journal review (which can take 3–12 months). Major preprint servers include arXiv (physics, math, computer science; founded 1991), bioRxiv (biology; 2013), medRxiv (medicine; 2019), and others. Preprints are NOT peer-reviewed and should not be treated as final scientific evidence. However, they enable priority-claiming, feedback from the community, and rapid knowledge sharing in fast-moving fields. Many journals now accept manuscripts previously posted as preprints.

Björk, B. C., Welling, P., Laakso, M., Majlender, P., Hedlund, T., & Guðnason, G. (2010). Open Access to the Scientific Journal Literature: Situation 2009. PLOS ONE, 5(6), e11273.

arXiv (2023). arXiv: e-Print Archive. Cornell University.

bioRxiv (2023). The Preprint Server for Biology. Cold Spring Harbor Laboratory.

open-access-publishing peer-review-process data-sharing-open-science preprint-servers


Stanford Presenteeism Scale

Clifford Koopman, Kenneth R. Pelletier, James Murray, and colleagues

The Stanford Presenteeism Scale (SPS-6) is a brief assessment tool measuring work productivity and performance among employees who are present at work despite health problems, personal issues, or other limitations. Developed by Koopman and colleagues in 2002, the SPS-6 quantifies the degree to which an employee's ability to concentrate, accomplish tasks, and maintain efficiency is compromised while working. Presenteeism—working while ill or impaired—is increasingly recognized as a significant occupational health concern with substantial economic and wellbeing consequences.

Koopman, C., Pelletier, K. R., Murray, J. F., Sharda, C. E., Berger, M. L., Turpin, R. S., ... & Bendel, T. (2002). Stanford Presenteeism Scale: Health status and employee productivity. Journal of Occupational and Environmental Medicine, 44(1), 14-20.

recovery-experience-questionnaire effort-reward-imbalance-scale copenhagen-burnout-inventory areas-of-worklife-scale workplace-incivility-scale