Optimization-assisted response surface methodology

Optimization-Assisted Response Surface Methodology

OA-RSM
RSM with optimization
desirability-based RSM
multi-response RSM optimization

1951 (RSM); 1980 (desirability-function optimization formalized)

Derringer & Suich (desirability function); Box & Wilson (RSM foundation)

Optimization-assisted RSM couples a second-order response surface model with a mathematical optimization routine — most commonly Derringer and Suich's desirability function, but also genetic algorithms or gradient-based solvers — to locate the factor settings that simultaneously satisfy multiple quality or performance objectives. The result is a data-driven recommendation for optimal process or product conditions, supported by a polynomial model fitted to a structured experimental design.

Continuous numerical response data from designed experiments


Optimization-assisted Six Sigma DMAIC

Optimization-Assisted Six Sigma Define-Measure-Analyze-Improve-Control

Optimization-integrated DMAIC
DMAIC with optimization
Six Sigma optimization framework
Opt-DMAIC

Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s

Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.

Process improvement framework with embedded optimization

Continuous process measurements, designed experiment data, response variables

Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27.

six-sigma-dmaic design-of-experiments response-surface-methodology taguchi-method statistical-process-control robust-six-sigma-dmaic


Optimization-assisted Taguchi method

Taguchi with meta-heuristic optimization

Base method: 1950s–1980s; optimization-assisted extensions: 1990s–2000s

Genichi Taguchi (base method); hybrid approach developed by engineering researchers in 1990s–2000s

The optimization-assisted Taguchi method extends Taguchi's robust design framework by coupling its orthogonal-array experiments with a secondary optimization algorithm — such as grey relational analysis, genetic algorithms, or particle swarm optimization — to simultaneously handle multiple response variables or to navigate a larger design space than pure Taguchi arrays can efficiently explore. The result is a structured, data-efficient experimental strategy that yields both robust parameter settings and globally near-optimal solutions.

Continuous or discrete process/product parameters; measured performance responses

Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.

Nalbant, M., Gokkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials & Design, 28(4), 1379-1385.

taguchi-method design-of-experiments response-surface-methodology grey-relational-analysis robust-taguchi-method multi-response-taguchi-method


OHLS

The Oral Health Literacy Scale (OHLS) is an assessment tool measuring patients' ability to understand, process, and act on oral health information. Originally developed as part of health literacy research by Rushton and colleagues, the OHLS evaluates comprehension of dental terminology, oral disease prevention concepts, and informed consent for dental treatment. The OHLS bridges communication gaps in dental care, identifying patients who may have difficulty understanding clinical explanations and treatment options, and enabling tailored patient education.

Rushton, V. E., Adams, N., & Orr, D. F. (2004). Oral health literacy and understanding of informed consent. British Dental Journal, 196(10), 577-581.


Oral History Method

oral history research
life history interviewing
oral testimony research
OHM

1948 (systematic practice); broader theorisation 1970s–1990s

Columbia University Oral History Research Office (Allan Nevins); later theorised by Alessandro Portelli and Donald Ritchie

The oral history method is a qualitative research approach in which researchers conduct in-depth, recorded interviews with individuals who have direct personal experience of a historical event, social process, or community life. It captures subjective perspectives, memory, and lived experience that written records rarely preserve, making it indispensable for recovering voices absent from official archives — particularly those of marginalised communities, minority groups, and ordinary people.

Audio/video-recorded interviews, transcripts, personal testimonies

Ritchie, D. A. (2015). Doing Oral History (3rd ed.). Oxford University Press.

Portelli, A. (1991). The Death of Luigi Trastulli and Other Stories: Form and Meaning in Oral History. State University of New York Press.

life-history-research narrative-analysis phenomenology ethnography historical-archival-research biographical-research


Oral History

life history interview
oral testimony
spoken history
oral narrative research

1948 (modern disciplinary form); broader roots in 19th-century folklore and anthropology

Allan Nevins (Columbia University Oral History Project, 1948); earlier roots in folk-life and anthropological fieldwork

Oral history is a qualitative research method that collects, preserves, and interprets first-person spoken accounts of past events, experiences, and social processes. By recording in-depth interviews with individuals who witnessed or participated in historical events, oral historians document perspectives that written records often exclude. The method bridges historical scholarship and social science, treating the narrator's memory, subjectivity, and voice as primary evidence rather than as limitations to be corrected.

Recorded interviews (audio/video), transcripts, personal documents, photographs

Ritchie, D. A. (2003). Doing Oral History: A Practical Guide (2nd ed.). Oxford University Press.

narrative-analysis ethnography phenomenology case-study thematic-analysis discourse-analysis


Oral Hygiene Index

OHI-S
simplified oral hygiene index
debris index
calculus index

The Oral Hygiene Index-Simplified (OHI-S) is a rapid, non-invasive assessment tool that evaluates the amount of plaque (debris) and calculus on tooth surfaces. Developed by Greene and Vermillion in 1964, it comprises two subscales: the Debris Index-Simplified (DI-S) measuring soft deposits and the Calculus Index-Simplified (CI-S) measuring hard deposits. The OHI-S is widely used in dental research, public health surveys, and clinical practice to assess oral hygiene status and evaluate the effectiveness of preventive interventions.

Greene, J. C., & Vermillion, J. R. (1964). The simplified oral hygiene index. Journal of the American Dental Association, 68(1), 7-13.

Vermillion, J. R., Arbuckle, G. B., & Spolsky, V. W. (1974). A comparison of the effectiveness of mechanical and chemotherapeutic plaque removal. Journal of the American Dental Association, 88(4), 862-865.

Löe, H. (1967). The gingival index, the plaque index and the retention index systems. Journal of Periodontology, 38(6), 610-616.

gingival-index dmft-index periodontal-probing tooth-mobility-assessment plaque-index


OIDP

The Oral Impacts on Daily Performances (OIDP) is an 8-item interview-administered instrument measuring the functional and social impact of oral conditions on everyday activities. Developed by Adulyanon and Sheiham in 1997, it captures how oral problems (pain, difficulty eating, appearance concerns) disrupt routine daily performances such as eating, speaking, cleaning teeth, sleeping, smiling, and work concentration. The OIDP is particularly valuable in developing countries and resource-limited settings where functional impairment is the primary concern.

Adulyanon, S., & Sheiham, A. (1997). Oral impacts on daily performances. In G. D. Slade (Ed.), Measuring Oral Health and Quality of Life (pp. 151-160). Chapel Hill: University of North Carolina.


ORB Feature Descriptor

Oriented FAST and Rotated BRIEF (ORB) Feature Descriptor

Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary Bradski

ORB (Oriented FAST and Rotated BRIEF) combines the FAST corner detector with the BRIEF binary descriptor to create a fast, rotation-invariant feature detector and descriptor. Introduced by Rublee et al. in 2011, ORB is designed as a free, efficient alternative to patented methods like SIFT and SURF, making it ideal for real-time and resource-constrained applications.

Local feature detector and binary descriptor

Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. International Conference on Computer Vision (ICCV), 2564–2571.

Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. European Conference on Computer Vision (ECCV), 430–443.

sift-feature-detection harris-corner-detection scale-space-theory blob-detection template-matching


Orbit Determination (Lambert's Problem)

Orbit Determination via Lambert's Problem

Lambert's problem
Lambert-Godstein trajectory problem

Lambert's problem is a classical astrodynamics boundary-value problem that determines an orbit connecting two points in space given a transfer time. Formulated by Johann Heinrich Lambert in the 18th century, it is fundamental to trajectory design for interplanetary missions and spacecraft maneuvers. The solution provides the orbital elements and velocities needed to transition between two positions.

Lambert, J. H. (1761). Acta Helvetica. Physico-Mathematico-Anatomico-Botanico-Medica.

Vallado, D. A., Crawford, P., Hujsak, R., & Kelso, T. S. (2006). Revisiting Spacetrack Report #3. In AIAA/AAS Astrodynamics Specialist Conference.

Gooding, R. H. (1990). A procedure for the solution of Lambert's orbital boundary-value problem. Celestial Mechanics and Dynamical Astronomy, 48(2), 145-165.

n-body-simulation hohmann-transfer gravity-assist cosmological-perturbation-theory


ORCID Researcher Identifier

ORCID Inc., a non-profit founded in 2010 by Liz Haak and others

ORCID (Open Researcher and Contributor ID) is a free, unique, persistent 16-digit identifier assigned to researchers that distinguishes them from others with the same or similar names. Launched in 2012 by ORCID Inc., a non-profit organization, the ORCID system addresses a critical problem in scholarly communication: name ambiguity. Millions of researchers worldwide share names (e.g., 'Smith, J.'). Without a unique identifier, citations and publications are difficult to attribute correctly, author H-indices are miscalculated, and researchers are credit for work they did not do. An ORCID iD is free, permanent, and owned by the researcher; it persists regardless of affiliation changes or career transitions.

Haak, L. L., Fenner, M., Paglione, L., Pentz, E., & Ratner, H. (2012). ORCID: A system to uniquely identify researchers. Learn. Publ., 25(4), 259–264.

ORCID Inc. (2024). About ORCID. https://orcid.org

Wolf, T., & Delgado, L. (2020). ORCID for research libraries: A collection of resources and research. First Monday, 25(5).

doi-system citation-management-tools altmetrics citation-analysis


Ordered Logit

Ordered Logistic Regression (Ordered Logit/Probit)

Sıralı Lojistik Regresyon (Ordered Logit/Probit)

McCullagh (proportional odds / cumulative model)

Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes.

McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142.

logistic-regression multinomial-logit ols-regression negative-binomial-regression probit-regression


Ordinal CFA

CFA for ordinal data
polychoric CFA
WLSMV CFA
categorical CFA

Ordinal confirmatory factor analysis (Ordinal CFA) tests a pre-specified factor structure when the observed indicators are ordinal — typically Likert-type survey items. By using polychoric correlations and robust estimators such as WLSMV, it avoids the bias that arises from treating categorical responses as 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. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.

confirmatory-factor-analysis exploratory-factor-analysis ordinal-exploratory-factor-analysis item-response-theory measurement-invariance polychoric-correlation


Ordinal Content Validity

ordinal CVI
Likert-scale content validity
ordinal expert rating validity
graded content validity

Ordinal content validity replaces the traditional binary (yes/no) expert relevance judgment with a graded, Likert-type rating scale, allowing richer expert opinion to be captured when evaluating whether scale items adequately represent the intended construct domain.

Wynd, C. A., Schmidt, B., & Schaefer, M. A. (2003). Two quantitative approaches for estimating content validity. Western Journal of Nursing Research, 25(5), 508–518.

Polit, D. F., & Beck, C. T. (2007). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 30(4), 459–467.

content-validity-index content-validity-ratio exploratory-factor-analysis scale-development item-analysis expert-judgment-methods


Ordinal Convergent Validity

OCV
convergent validity for ordinal scales
polychoric convergent validity
ordinal AVE

1959 (validity framework); ordinal adaptation 1990s–2000s

Polychoric/tetrachoric correlation tradition (Pearson, 1900s); validity framework formalized by Campbell & Fiske (1959)

Ordinal convergent validity assesses the degree to which indicators of the same latent construct correlate strongly with each other when those indicators are measured on ordinal (e.g., Likert-type) scales. It adapts standard convergent validity procedures — factor loadings, average variance extracted, and HTMT ratios — to account for the discrete, bounded nature of ordinal response categories using polychoric correlations and ordinal-appropriate estimation methods.

Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373.

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.

convergent-validity discriminant-validity ordinal-confirmatory-factor-analysis ordinal-reliability-analysis measurement-invariance confirmatory-factor-analysis


Ordinal Cronbach's Alpha

alpha for ordinal data
polychoric alpha
ordinal reliability coefficient
alpha based on polychoric correlations

Ordinal Cronbach's alpha is a reliability coefficient computed from polychoric or polyserial correlations rather than Pearson correlations, making it appropriate for Likert-type and other ordinal item response data. It corrects the systematic downward bias that standard Cronbach's alpha produces when items are treated as continuous but are actually ordinal.

Internal consistency reliability coefficient

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.

Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research and Evaluation, 17(3), 1–13.

cronbachs-alpha mcdonalds-omega ordinal-reliability-analysis ordinal-item-analysis polychoric-correlation confirmatory-factor-analysis


Ordinal Differential Item Functioning

Ordinal Differential Item Functioning Analysis

ordinal DIF
polytomous DIF
DIF for ordered categories
ordinal logistic DIF

Zumbo (logistic extension) and Penfield (Mantel generalization)

Ordinal differential item functioning analysis detects whether an ordered-category item (such as a Likert-scale question) functions differently across demographic or cultural groups after controlling for the latent trait being measured. It extends classical binary DIF methods to polytomous response formats common in psychological and educational scales.

Item bias detection for ordered-category items

Ordinal polytomous item scores (e.g., Likert scales)

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. Ottawa: Directorate of Human Resources Research and Evaluation, Department of National Defense.

Penfield, R. D. (2001). Assessing differential item functioning among multiple groups: A comparison of three Mantel-Haenszel procedures. Applied Measurement in Education, 14(3), 235-259.

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


Ordinal Discriminant Validity

Ordinal Discriminant Validity Assessment

discriminant validity for ordinal data
polychoric discriminant validity
ordinal HTMT
ordinal AVE-based discriminant validity

1959 (concept); 2000s–2010s (ordinal adaptations)

Campbell & Fiske (discriminant validity concept); adapted for ordinal data by subsequent psychometricians

Ordinal discriminant validity assesses whether a latent construct measured by ordinal (Likert-type) items is empirically distinct from other constructs in the same instrument. It applies polychoric correlations and ordinal-appropriate factor loadings to standard discriminant validity criteria such as the Fornell-Larcker rule and the Heterotrait-Monotrait ratio (HTMT), ensuring that validity conclusions are not distorted by the non-continuous nature of ordered-response data.

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

discriminant-validity convergent-validity ordinal-confirmatory-factor-analysis ordinal-reliability-analysis construct-validity ordinal-exploratory-factor-analysis


Ordinal EFA

ordinal factor analysis
polychoric EFA
categorical EFA
EFA for ordinal data

Ordinal exploratory factor analysis discovers latent factors underlying a set of ordinal items — typically Likert scales — by computing polychoric correlations among the items and then applying a weighted least squares estimator. It avoids the distortions that arise when continuous EFA methods are naively applied to ordered categorical responses.

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

exploratory-factor-analysis confirmatory-factor-analysis polychoric-correlation item-response-theory structural-equation-modeling cronbach-alpha


Ordinal Generalizability Theory

Ordinal G-theory
G-theory for ordinal data
ordinal variance component analysis
G-study for ordered categorical data

Ordinal generalizability theory extends classical G-theory to the analysis of reliability and measurement error when item responses are ordered categorical (e.g., Likert-type) rather than continuous. It partitions score variance into components attributable to persons, facets, and their interactions, while accounting for the discrete, bounded nature of ordinal rating scales.

Ordinal (Likert-type, rating scale) scores

Brennan, R. L. (2001). Generalizability Theory. Springer.

Mushquash, C., & O'Connor, B. P. (2006). SPSS and SAS programs for generalizability theory analyses. Behavior Research Methods, 38(3), 542–547.

generalizability-theory ordinal-reliability-analysis ordinal-item-response-theory ordinal-confirmatory-factor-analysis multilevel-reliability-analysis cronbachs-alpha


Ordinal Item Analysis

item analysis for ordinal data
polytomous item analysis
Likert item analysis
OIA

Classical test theory tradition (Guilford, Nunnally, and others)

Ordinal item analysis evaluates each individual item in a rating-scale or Likert-type instrument using descriptive and correlational statistics suited to ordered categorical response formats. It guides item selection and refinement by flagging items with problematic difficulty, poor discrimination, or low corrected item-total correlations before reliability and validity studies proceed.

Ordinal (Likert-type, rating scale) item responses

Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.

Drasgow, F., Levine, M. V., Tsien, S., Williams, B. & Mead, A. D. (1995). Fitting polytomous item response theory models to multiple-choice tests. Applied Psychological Measurement, 19(2), 143–165.

ordinal-reliability-analysis ordinal-cronbachs-alpha ordinal-exploratory-factor-analysis item-response-theory differential-item-functioning scale-development


Ordinal IRT

polytomous IRT
ordinal IRT models
graded response models
ordinal latent trait models

Fumiko Samejima (Graded Response Model, 1969); Gerhard Fischer & Georg Rasch lineage for partial credit

Ordinal item response theory (ordinal IRT) comprises a family of probabilistic models — most notably the Graded Response Model and the Partial Credit Model — that relate a respondent's standing on a latent trait to the probability of choosing each ordered response category on a polytomous item. It extends classical IRT beyond dichotomous items to the Likert-type and rating-scale items that dominate psychometric measurement.

Probabilistic latent trait model for ordered polytomous responses

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

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.

item-response-theory graded-response-model partial-credit-model confirmatory-factor-analysis ordinal-confirmatory-factor-analysis differential-item-functioning


Ordinal Logistic Regression

Ordinal Logistic Regression (Proportional-Odds Model)

proportional-odds model
cumulative link model
ordered logit
OLR

Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.

Ordinal outcome (3+ ranked categories), continuous or categorical predictors

McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.

Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). John Wiley & Sons.

logistic-regression multinomial-logistic-regression probit-model ols-regression quantile-regression generalized-linear-model


Ordinal McDonald's omega

Ordinal McDonald's Omega Reliability Coefficient

omega ordinal
ordinal omega
polychoric omega
omega for ordinal data

Bruno D. Zumbo, Anne M. Gadermann, and Cornelia Zeisser (building on McDonald's 1999 omega framework)

Ordinal McDonald's omega is a reliability coefficient designed for Likert-type and other ordinal rating scales. Unlike Cronbach's alpha, it bases its calculation on polychoric correlations among items — capturing the true latent relationships between ordinal responses — and uses factor-analytic loadings to estimate how much of the composite score variance is attributable to a common factor.

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.

cronbach-alpha mcdonalds-omega confirmatory-factor-analysis polychoric-correlation ordinal-alpha item-response-theory


Ordinal Measurement Invariance

ordinal MI
measurement invariance for ordinal data
ordinal CFA invariance
categorical measurement invariance

Ordinal measurement invariance testing evaluates whether a multi-group confirmatory factor model holds equivalent measurement properties across groups when scale items are ordinal — such as Likert-type response scales. It uses polychoric correlations and categorical estimators (WLSMV/DWLS) rather than Pearson-based methods, correcting the systematic bias that arises when ordinal data are treated as continuous.

Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.

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


Ordinal Nomological Validity

nomological validity for ordinal data
ordinal nomological network
construct network validity (ordinal)
ordinal criterion-related validity

1955 (concept); ordinal application 1990s–present

Cronbach & Meehl (nomological network concept); ordinal extension in modern psychometrics

Ordinal nomological validity examines whether a construct measured with ordinal items (e.g., Likert-type scales) behaves in theoretically predicted ways within a nomological network — a web of expected relationships with other constructs and criteria — using methods suited to ordinal data rather than assuming continuous measurement.

Ordinal indicators (Likert, rating scales)

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.

Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061–1071.

nomological-validity ordinal-convergent-validity ordinal-discriminant-validity ordinal-construct-validity confirmatory-factor-analysis ordinal-measurement-invariance


Ordinal Priority Approach

The Ordinal Priority Approach (OPA) is a family of methods that derive criteria weights directly from ordinal rankings rather than cardinal (numerical) preferences. Instead of asking decision-makers to assign exact weight values or ratio comparisons, OPA asks only: which criterion is most important, which is second, etc. The method then converts this ordinal ranking into numerical weights using geometric or statistical formulas.

Edwards, W. (1977). Use of multiattribute utility measurement for social decision making. In D. E. Bell, R. L. Keeney, & H. Raiffa (Eds.), Conflicting objectives in decisions (pp. 247-307). Wiley.

Kobus, J., & Ware, J. C. (2013). Ranking ordinal preferences: A geometric approach. Decision Sciences, 44(1), 53-76.

bwm ahp promethee electre rank-order-centroid


Ordinal Rasch Model

Ordinal Rasch Model (Rating Scale and Partial Credit Models)

Rating Scale Model
Partial Credit Model
RSM
PCM

David Andrich (RSM, 1978); Geoff Masters (PCM, 1982)

The ordinal Rasch model extends the dichotomous Rasch framework to items with ordered response categories such as Likert-type scales. It places both persons and items on a shared interval-level metric, enabling principled measurement from ordinal data while checking whether items function consistently across all response thresholds.

Item response model for ordered categories

Ordinal polytomous item responses (Likert, rating scales)

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

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

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


Ordinal Regression

Ordinal Logistic Regression (Proportional Odds Model)

proportional odds model
ordered logit
ordinal logistic regression
Ordinal Regresyon (Proportional Odds)

Agresti (textbook treatment); proportional odds model

Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.

Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.

Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Sage.

logistic-regression multinomial-logistic-regression ols-regression poisson-regression latent-class-analysis


Ordinal Reliability Analysis

ordinal alpha
polychoric reliability
reliability for ordinal scales
ORA

Ordinal reliability analysis estimates the internal consistency of scales whose items are measured on ordered-category (Likert-type) response formats. By basing computations on polychoric correlations rather than Pearson correlations, it corrects for the attenuation that standard Cronbach's alpha produces when responses are discrete and non-normal.

Internal consistency reliability estimation

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.

Gadermann, A. M., Guhn, M. & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research & Evaluation, 17(3), 1–13.

cronbach-alpha mcdonald-omega confirmatory-factor-analysis polychoric-correlation item-response-theory exploratory-factor-analysis


Ordinal Scale Development

Likert scale development
ordinal measurement scale construction
ordinal item development
polytomous scale construction

1932 (Likert format); 1990s–2000s (ordinal-specific psychometric methods)

Rensis Likert (foundational ordinal response format); modern ordinal methodology codified by DeVellis and Finney & DiStefano

Ordinal scale development is the systematic construction and validation of multi-item measurement instruments whose response options form an ordered but not necessarily equal-interval sequence — most commonly Likert-type formats (e.g., 1 = Strongly Disagree to 5 = Strongly Agree). It applies psychometric techniques that respect the ordinal nature of items rather than treating them as continuous.

DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). SAGE Publications.

Finney, S. J. & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269–314). Information Age Publishing.

scale-development ordinal-reliability-analysis ordinal-confirmatory-factor-analysis ordinal-exploratory-factor-analysis item-response-theory cronbachs-alpha


Ordinal Test-Retest Reliability

Ordinal Test-Retest Reliability Analysis

rank-based test-retest reliability
ordinal temporal consistency
Spearman test-retest reliability
weighted kappa test-retest

Multiple contributors (Spearman, Cohen, Shrout & Fleiss)

Ordinal test-retest reliability quantifies how consistently an ordinal measurement instrument — such as a Likert-scale questionnaire or a rating tool — ranks or scores the same participants across two separate administrations separated by a stable interval, using correlation and agreement statistics suited to ordered categorical data.

Ordinal (Likert, rating scales, ranked responses)

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.

Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220.

test-retest-reliability intraclass-correlation-coefficient cronbachs-alpha ordinal-reliability-analysis weighted-kappa mcdonalds-omega


Ordinary Kriging

OK
kriging interpolation
geostatistical interpolation
BLUE spatial predictor

Georges Matheron (formalising D.G. Krige's empirical work)

Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point.

Continuous spatial point data with coordinates

Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.

Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). Wiley-Interscience.

kriging universal-kriging co-kriging spatial-autocorrelation kernel-density-estimation geographically-weighted-regression


Ordinary Least Squares

OLS
OLS regression
linear least squares
classical linear regression
least squares estimation

Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809)

Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority from 1795), OLS is provably optimal under the Gauss-Markov theorem: given its assumptions, it yields the Best Linear Unbiased Estimator (BLUE) of the regression coefficients.

Legendre, A.-M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la Méthode des moindres quarrés, pp. 72–80.]

Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Perthes & Besser, Hamburg.

Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.

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

multiple-linear-regression weighted-least-squares generalized-least-squares ridge-regression lasso-regression simple-linear-regression robust-regression instrumental-variables


ORESTE

ORESTE (Organisation, Rangement Et Synthèse de données rElaTionnEllEs) is a ranking multi-criteria decision-making (MCDM) method introduced by Roubens, M. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Outranking (weak order aggregation via Besson rank)

Roubens, M. (1982). Preference relations on actions and criteria in multicriteria decision making. European Journal of Operational Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


ORIC

ORIC
Organizational Readiness for Change
ORIC-12

Christopher M. Shea, PhD; Sarah R. Jacobs, PhD; Dean A. Esserman, PhD; and colleagues

The Organizational Readiness for Implementing Change (ORIC) is a 12-item self-report measure that assesses organizational readiness to implement evidence-based practices and innovations. Developed by Shea and colleagues in 2014, the ORIC measures two critical dimensions of organizational readiness: Change Commitment (the extent to which staff and leadership are motivated and dedicated to implementing change) and Change Efficacy (the extent to which staff believe they have the capability and resources to successfully implement the change). The ORIC is grounded in implementation science theory and has demonstrated strong psychometric properties and predictive validity for implementation success across healthcare, mental health, and organizational settings.

Shea, C. M., Jacobs, S. R., Esserman, D. A., Wagner, S. L., & Kraemer, D. F. (2014). Organizational readiness for implementing change: A psychometric assessment of a new measure. Implementation Science, 9, 26.

evidence-based-practice-attitude implementation-leadership-scale implementation-climate-scale perceived-organizational-readiness knowledge-to-action-scale


The Organizational Citizenship Behavior Scale (OCBS) is a 16-item instrument measuring discretionary employee contributions beyond formal job requirements. Developed by Organ in 1988 and operationalized by Williams and Anderson in 1991, the OCBS assesses two dimensions: helping behaviors toward coworkers and support for the organization.

Organ, D. W. (1988). Organizational citizenship behavior: The good soldier syndrome. Lexington Books.

Williams, L. J., & Anderson, S. E. (1991). Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors. Journal of Management, 17(3), 601-617.

organizational-trust-scale employee-engagement-survey ethical-leadership-scale knowledge-sharing-scale


The Porter Organizational Commitment Questionnaire (OCQ) measures an employee's emotional attachment to, identification with, and involvement in their employing organization. Developed by Porter and colleagues in 1974, the original 15-item scale captures affective commitment—the genuine belief in and support for the organization's goals and values. The OCQ is one of the most extensively researched and validated commitment measures, predicting retention, absenteeism, and performance.

Porter, L. W., Steers, R. M., Mowday, R. T., & Boulian, P. V. (1974). Organizational commitment, job satisfaction, and turnover among psychiatric technicians. Journal of Applied Psychology, 59(5), 603–609.

Mowday, R. T., Porter, L. W., & Steers, R. M. (1982). Employee-organization linkages: The psychology of commitment, absenteeism, and turnover. Academic Press.

Meyer, J. P., & Allen, N. J. (1997). Commitment in the workplace: Theory, research, and application. Sage Publications.

perceived-organizational-support job-descriptive-index leader-member-exchange-scale psychological-capital-questionnaire career-adapt-abilities-scale


Organizational Commitment Scale

Organizational Commitment Scale (OCS) - Three-Component Model

The Organizational Commitment Scale (OCS), developed by Meyer and Allen in 1991, measures three distinct dimensions of organizational commitment: affective commitment (emotional attachment), continuance commitment (perceived cost of leaving), and normative commitment (sense of obligation). This three-component model has become foundational in understanding employee retention, engagement, and organizational attachment.

Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of organizational commitment. Human Resource Management Review, 1(1), 61-89.

Meyer, J. P., & Allen, N. J. (2004). Employee commitment, motivation, and well-being. In N. Anderson, D. S. Ones, H. K. Sinangil, & C. Viswesvaran (Eds.), Handbook of work and organizational psychology (pp. 274-296). London: SAGE Publications.

job-satisfaction-survey psychological-safety-scale job-demands-resources-scale transformational-leadership-scale servant-leadership-scale


The Organizational Culture Assessment Instrument (OCAI) is a 24-item diagnostic tool that identifies dominant organizational culture types based on the Competing Values Framework (CVF). Developed by Kim S. Cameron and Robert E. Quinn, the OCAI measures cultures across four archetypes: Clan, Adhocracy, Market, and Hierarchy.

Cameron, K. S., & Quinn, R. E. (2011). Diagnosing and changing organizational culture: Based on the competing values framework (3rd ed.). Jossey-Bass.

Quinn, R. E., & Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis. Management Science, 29(3), 363-377.

organizational-trust-scale employee-engagement-survey authentic-leadership-scale innovation-climate-scale


Organizational Justice Scale

Organizational Justice Scale (OJS) - Multidimensional Framework

The Organizational Justice Scale (OJS) measures employees' perceptions of fairness in organizational settings across four dimensions: distributive justice (fairness of outcomes), procedural justice (fairness of decision-making processes), interpersonal justice (respectful and dignified treatment), and informational justice (honest and adequate communication). Developed by Colquitt (2001) and building on earlier work by Moorman (1991), the OJS assesses how fairly employees perceive they and their work are treated, predicting organizational commitment, citizenship behavior, and turnover.

Colquitt, J. A. (2001). On the dimensionality of organizational justice: a construct validation of a measure. Journal of Applied Psychology, 86(3), 386-400.

Moorman, R. H. (1991). Relationship between organizational justice and organizational citizenship behaviors: Do fairness perceptions influence employee citizenship? Journal of Applied Psychology, 76(6), 845-855.

organizational-commitment-scale psychological-safety-scale transformational-leadership-scale servant-leadership-scale job-satisfaction-survey


Organizational Learning Scale

Seng Chee Goh; Peter Senge (foundational theory)

The Organizational Learning Scale (OLS) is a 21-item instrument measuring organizational capacity to learn and adapt, based on Senge's learning organization framework. Formalized by Goh in 2003, the OLS assesses five dimensions: systems thinking, shared vision, team learning, shared mental models, and personal mastery.

Senge, P. M. (1990). The fifth discipline: The art & practice of the learning organization. Doubleday.

Goh, S. C. (2003). Improving organizational learning capacity over time. Learning Organization, 10(4), 216-229.

innovation-climate-scale knowledge-sharing-scale organizational-culture-assessment employee-engagement-survey


Organizational Resilience Scale

Organizational Resilience Capability Assessment Scale

Resilience Scale
Organizational Adaptability Scale
Crisis Preparedness Scale

Karl Weick, Kathleen Sutcliffe, and subsequent organizational scholars

Organizational Resilience refers to an organization's capacity to anticipate disruptions, withstand shocks, and adapt effectively to changing circumstances while maintaining core identity and functionality. Weick and Sutcliffe (2007) argue that resilience is not primarily about avoiding disruption but about developing capability to sense threats early, respond rapidly, and learn from shocks. The COVID-19 pandemic exposed organizational resilience gaps: firms with diversified supply chains, flexible workforce arrangements, and adaptive cultures recovered faster than those with fragile, optimized-for-efficiency structures. This scale measures organizational resilience across three dimensions: readiness (preparation for uncertainty), response capability (speed and agility in crisis), and adaptive learning (capturing and applying lessons).

Weick, K. E., & Sutcliffe, K. M. (2007). Managing the unexpected: Resilient performance in an age of uncertainty. Jossey-Bass.

Coutu, D. L. (2002). How resilience works. Harvard Business Review, 80(5), 46–52.

Stephens, J. P., & Carmeli, A. (2016). Leveraging employee engagement for competitive advantage: The human resource management perspective. Journal of Organizational Effectiveness, 3(2), 171–189.

dynamic-capabilities-scale supply-chain-integration-scale corporate-governance-questionnaire knowledge-management-scale strategic-orientation-scale


Organizational Trust Scale

The Organizational Trust Scale (OTS) is a 12-item instrument designed to measure interpersonal trust and organizational confidence across four dimensions. Developed by Aneil K. Mishra in 1996, the scale addresses how employees perceive trustworthiness in their organization and leadership.

Mishra, A. K. (1996). Organizational responses to crisis: The role of mutual trust and top management teams. Academy of Management Journal, 39(4), 842-865.

Cook, K. S., Hardin, R., & Levi, M. (2001). Cooperation without trust?. Russell Sage Foundation.


Original Research Article

Original Research Article (Primary Research Publication)

research paper
empirical article
primary research
journal article

An original research article is the primary vehicle for reporting new empirical findings in a discipline. Following the IMRaD structure (Introduction, Methods, Results, and Discussion), it represents a researcher's novel data, analysis, and interpretation. The journal article format has been the gold standard for scientific communication since the establishment of the Philosophical Transactions of the Royal Society in 1665.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.

EQUATOR Network (2024). Enhancing the QUAlity and Transparency Of health Research. http://www.equator-network.org

American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.

systematic-review-article meta-analysis-article literature-review-article peer-review-process imrad-structure


OES

Multiple authors across orthodontic esthetic research

The Orofacial Esthetic Scale (OES) is a multi-item instrument measuring perception of esthetic quality of teeth, smile, and facial appearance in relation to oral conditions. Developed across multiple research groups in orthodontic and restorative dentistry, the OES quantifies patient and clinician satisfaction with dental esthetics, guiding treatment planning and outcome assessment in cosmetic and orthodontic interventions. The OES captures the patient-centered esthetic perspective, complementing objective dental measurements and enabling assessment of esthetic outcome success.

Bergström, K., & Bergström, J. (2003). A 6-year longitudinal study of dental health in patients with Crohn's disease. Journal of Periodontology, 74(7), 985-989.


Orthodontic Cephalometry

cephalometric analysis
cephalometric radiography
cephalogram

Orthodontic cephalometry is a standardized radiographic technique that produces a lateral or postero-anterior skull radiograph from a fixed source-to-film distance and patient position. Introduced by Benjamin Broadbent in 1931, cephalometric analysis enables systematic measurement of skeletal and dental relationships to assess malocclusion, plan treatment, and monitor growth and treatment changes. The technique remains fundamental to orthodontic diagnosis and treatment planning.

Broadbent, B. H. (1931). A new x-ray technique and its application to orthodontia. Angle Orthodontist, 1(2), 45-66.

Downs, W. B. (1948). Variations in facial relationships: their anatomical and dental significance. American Journal of Orthodontics, 34(10), 812-840.

McNamara Jr, J. A. (1984). A method of cephalometric evaluation. American Journal of Orthodontics, 86(6), 449-469.

bitewing-radiography occlusal-analysis shade-selection-dentistry temporomandibular-joint-analysis


OQLiQ

The Orthognathic Quality of Life Questionnaire (OQLiQ) is a 22-item, condition-specific instrument measuring quality of life in patients with dentofacial deformity (severe malocclusion, skeletal asymmetry) before and after orthognathic surgery (surgical correction of jaw and bite discrepancies). Developed by Cunningham and colleagues in 2000, the OQLiQ captures impacts specific to severe malocclusion: functional limitations (eating, speaking), emotional distress about appearance, social embarrassment, and expectations for change following surgery. It has become the standard outcome measure in orthognathic surgery trials.

Cunningham, S. J., Garratt, A. M., & Hunt, N. P. (2000). Development of a condition-specific quality of life measure for patients with dentofacial deformity: I. Reliability of the Orthognathic Quality of Life Questionnaire. Community Dentistry and Oral Epidemiology, 28(3), 195-201.


OSPF

OSPF is a link-state interior gateway protocol (IGP) for routing within an autonomous system. Introduced by John Moy in 1998, OSPF converges faster than distance-vector protocols and supports equal-cost multipath (ECMP). It remains widely deployed in enterprise and ISP networks for intra-domain routing, though IS-IS is increasingly preferred in large backbones.

Moy, J. T. (1998). OSPF Version 2. RFC 2328.

Coltun, R., Ferguson, D., Moy, J., & Lindem, A. (2008). OSPF for IPv6. RFC 5340.


Oswestry Disability Index

The Oswestry Disability Index (ODI) is a disease-specific measure of disability due to low back pain, originally developed by Fairbank and colleagues in 1980. It is one of the most widely used outcome measures in spine care, enabling clinicians and researchers to quantify the functional impact of low back pain and track treatment response in patients across acute, subacute, and chronic presentations.

Oswestry, J. D., Proudfoot, S. J., Everleigh, S., & Sparkes, V. (1980). An automatic method for measuring vertebral interbody disc heights. Clinical Biomechanics, 5(2), 104–109.

Fairbank, J. C., Couper, J., Davies, J. B., & O'Brien, J. P. (1980). The Oswestry Low Back Pain Disability Questionnaire. Physiotherapy, 66(8), 271–273.

Hudson-Cook, N., Tomes-Nicholson, K., & Breen, A. C. (1989). A re-evaluation of the Oswestry Low Back Pain Disability Questionnaire. Spine, 14(9), 957–966.

womac ndi-neck-disability dash-outcome-measure barthel-adl-index


Out-of-Distribution Detection

OOD Detection
Novelty Detection
Open-Set Recognition
Dağılım Dışı Tespit

Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than silently produce unreliable predictions, making them foundational to trustworthy and safe AI deployment in high-stakes domains.

Reliability and safety method for neural networks

Input: Pre-trained neural network softmax scores or feature representations

Output: Binary flag or anomaly score indicating in-distribution vs. out-of-distribution

Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations.


Outcome Rating Scale

The Outcome Rating Scale (ORS) is a 4-item ultra-brief symptom and wellbeing measure designed to track subjective improvement across individual, interpersonal, social, and overall functioning dimensions. Developed by Miller and Duncan, the ORS uses visual analog scales to enable session-by-session outcome monitoring in clinical practice and research. It is paired with the Session Rating Scale (SRS) in measurement-based care protocols to simultaneously track what clients feel and how they are functioning.

Miller, S. D., Duncan, B. L., Brown, J., Sparks, J. A., & Claud, D. A. (2003). The Outcome Rating Scale: Preliminary validity studies of a brief, visual, general measure of session effectiveness. Journal of Brief Therapy, 5(2), 23–33.

Blakely, C. H., & Dziadosz, C. M. (2015). Outcome Rating Scale and Session Rating Scale. In G. P. Koocher, J. C. Norcross, & S. S. Hill (Eds.), Psychologists' desk reference (3rd ed., pp. 533–538). Oxford University Press.

session-rating-scale working-alliance-inventory therapeutic-alliance-scale common-factors-questionnaire


Overactive Bladder Questionnaire

The OAB-q is a patient-reported outcome measure designed to assess the symptoms and impact of overactive bladder syndrome on health-related quality of life. Developed by Coyne and colleagues and first published in 2005, it exists in both long-form (33 items) and short-form (SF, 25 items) versions. The OAB-q is internationally validated and widely used in clinical research, pharmaceutical trials, and specialist urology and gynecology practice.

Coyne, K. S., Matza, L. S., & Payne, K. A. (2005). The Overactive Bladder Questionnaire (OAB-q): validation and psychometric properties. Neurourology and Urodynamics, 24(3), 215–225.

Coyne, K. S., Thompson, C. L., Lai, J. S., & Sexton, C. C. (2015). An overactive bladder symptom and health-related quality of life short-form measure: validation and psychometric evaluation. Eur Urol, 57(4), 588–596.


Overall Equipment Effectiveness

OEE
Equipment Effectiveness Index
Machine Effectiveness Ratio
Toplam Ekipman Etkinliği

Overall Equipment Effectiveness (OEE) is a composite key performance indicator that quantifies how effectively a manufacturing operation uses its equipment relative to its full potential. Developed by Seiichi Nakajima in 1988 as a cornerstone metric of Total Productive Maintenance (TPM), OEE multiplies three loss factors—Availability, Performance, and Quality—to yield a single percentage that benchmarks actual productive output against ideal output.

Nakajima, S. (1988). Introduction to TPM: Total Productive Maintenance. Productivity Press.


Overlapping Generations Model

The Overlapping Generations Model, pioneered by Paul Samuelson in 1958 and extended by Peter Diamond in 1965, is a macroeconomic framework where successive generations of individuals live for finite periods and coexist at any point in time. It addresses how consumption, savings, and capital accumulation evolve across generations and how monetary and fiscal policy affects intergenerational distribution.

Diamond, P. A. (1965). National Debt in a Neoclassical Growth Model. American Economic Review, 55(5), 1126–1150.

Samuelson, P. A. (1958). An Exact Consumption-Loan Model of Interest with or without the Social Contrivance of Money. Journal of Political Economy, 66(6), 467–482.


Overtourism Perception Scale

OPS
Tourism Congestion Scale
Crowding Perception Scale

The Overtourism Perception Scale (OPS) measures residents' and visitors' concerns about excessive tourism, measuring crowding, environmental degradation, cultural erosion, infrastructure strain, and resulting experience quality diminishment. Rooted in carrying capacity theory (Shelby & Heberlein, 1986) and resident impact perception research (Andereck et al., 2005), the OPS operationalizes overtourism as a multifaceted phenomenon affecting both visitor experience satisfaction and community wellbeing. Overtourism is increasingly critical for destination sustainability; the OPS enables monitoring of perception trends and targeting of mitigation strategies (visitor dispersal, infrastructure investment, capacity management) before crises (resident backlash, environmental damage, reputation loss) occur.

Shelby, B., & Heberlein, T. A. (1986). Carrying capacity in recreation settings. University of Oregon Press. Also see: Journal of Leisure Research, 21(4), 318-339.

Andereck, K. L., Valentine, K. M., Knopf, R. C., & Vogt, C. A. (2005). Residents' perceptions of community tourism impacts. Annals of Tourism Research, 32(4), 1056-1076.

Sharpley, R. (2012). Consumerism and tourism. Journal of Travel & Tourism Marketing, 29(2), 210-235.

Doxey, G. V. (1976). When enough's enough: The natives are restless in old Niagara. Heritage Canada, 2(2), 26-27.

destination-image-scale tourist-satisfaction-scale place-attachment-scale perceived-value-scale-tourism tourist-loyalty-scale


OWA

OWA (Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 1988; GIS extension 1997. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Parameterised additive aggregation with reordered criterion weights (AND ↔ WLC ↔ OR continuum)

Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics


Oxford Hip Score

Oxford Hip Score - Hip Replacement Outcome

Hip replacement outcome and functional assessment

David W. Murray and colleagues at University of Oxford

The Oxford Hip Score (OHS) is a brief, validated self-report questionnaire developed by Murray and colleagues at the University of Oxford beginning in 1996 to measure outcomes following hip replacement surgery. The OHS comprises 12 items assessing hip pain, hip-related functional limitations, and quality of life in patients undergoing hip arthroplasty. It is the most widely used patient-reported outcome measure for hip replacement in both clinical practice and research.

Murray, D. W., Fitzpatrick, R., Rogers, K., Pandit, H., Beard, D. J., Carr, A. J., & Dawson, J. (2007). The use of the Oxford Hip and Knee Scores. Journal of Bone and Joint Surgery, 89(8), 1010-1014.

Dawson, J., Fitzpatrick, R., Carr, A., & Murray, D. (1996). Questionnaire on the perceptions of patients about total hip replacement. Journal of Bone and Joint Surgery, 78(2), 185-190.

Fitzgerald, R. H. (2004). The Oxford Hip Score. Journal of Bone and Joint Surgery, 86(5), 671-672.


Oxford Knee Score

Oxford Knee Score - Knee Replacement Outcome

Knee replacement outcome and functional assessment

The Oxford Knee Score (OKS) is a brief, validated self-report questionnaire developed by Murray and colleagues at the University of Oxford in 1998 to measure outcomes following knee replacement surgery. The OKS comprises 12 items assessing knee pain, knee-related functional limitations, and quality of life in patients undergoing total knee arthroplasty. It is the primary patient-reported outcome measure for knee replacement in international orthopedic research and clinical practice.

Dawson, J., Fitzpatrick, R., Murray, D., & Carr, A. (1998). Questionnaire on the perceptions of patients about total knee replacement. Journal of Bone and Joint Surgery, 80(1), 63-69.

Jenkinson, C., Coulter, A., & Bruster, S. (2002). The Picker Patient Experience Questionnaire: development and validation using data from in-patient surveys in five countries. International Journal for Quality in Health Care, 14(5), 353-358.

Beard, D. J., Harris, K., Dawson, J., Doll, H., Murray, D. W., Carr, A. J., & Price, A. J. (2015). Meaningful changes for the Oxford Hip and Knee Scores after joint replacement surgery. Journal of Clinical Epidemiology, 68(1), 73-79.


P-ARAS

P-ARAS (Plithogenic extension of P-ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)

Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels


P-AROMAN

P-AROMAN (Plithogenic extension of P-AROMAN) is a ranking multi-criteria decision-making (MCDM) method introduced by Bošković et al. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Bošković et al. (2023). Plithogenic Alternative Ranking Order Method Accounting for two-step Normalization. Plithogenic Logic and Computation


P-COCOSO

P-COCOSO (P-CoCoSo — Plithogenic extension of P-COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-CODAS

P-CODAS (Plithogenic extension of P-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-COPRAS

P-COPRAS (Plithogenic extension of P-COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-DNMA

P-DNMA (Plithogenic extension of P-DNMA) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-EDAS

P-EDAS (Plithogenic extension of P-EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-GRA

P-GRA (Plithogenic extension of P-GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-MABAC

P-MABAC (Plithogenic extension of P-MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-MARCOS

P-MARCOS (Plithogenic extension of P-MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-MAUT

P-MAUT (Plithogenic extension of P-MAUT) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-MOORA

P-MOORA (Plithogenic extension of P-MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-MULTIMOORA

P-MULTIMOORA (Plithogenic extension of P-MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-OCRA

P-OCRA (Plithogenic extension of P-OCRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-PROMETHEE

P-PROMETHEE (Plithogenic extension of P-PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Smarandache, F. (2019). Plithogenic MCDM methods. Infinite Study


P-PSI

P-PSI (Plithogenic extension of P-PSI) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-RAFSI

P-RAFSI (Plithogenic extension of P-RAFSI) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-RAWEC

P-RAWEC (Plithogenic extension of P-RAWEC) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-ROV

P-ROV (Plithogenic extension of P-ROV) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-SAW

P-SAW (Plithogenic extension of P-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-SPOTIS

P-SPOTIS (Plithogenic extension of P-SPOTIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-TODIM

P-TODIM (Plithogenic extension of P-TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-TOPSIS

P-TOPSIS (Plithogenic extension of P-TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-Value and Statistical Significance

P-Value and the Concept of Statistical Significance in Hypothesis Testing

p-value
significance test
statistical significance
alpha level

The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05).

Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.

Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337.

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129–133.

null-hypothesis type-i-type-ii-error effect-size statistical-power multiple-comparisons-problem


P-VIKOR

P-VIKOR (Plithogenic extension of P-VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-WASPAS

P-WASPAS (Plithogenic extension of P-WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-WISP

P-WISP (Plithogenic extension of P-WISP) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


P-WPM

P-WPM (Plithogenic extension of P-WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


Patient Assessment of Constipation Quality of Life Questionnaire

Marquis, P., De La Loge, C., Dubois, D., et al.

The Patient Assessment of Constipation Quality of Life (PAC-QoL) is a validated, patient-reported outcome measure designed to assess the impact of functional constipation on physical, psychological, and social well-being. Developed by Marquis and colleagues in 2005, the PAC-QoL comprises 28 items organized into four domains: Physical Discomfort, Psychosocial Discomfort, Worries and Concerns, and Satisfaction. The PAC-QoL is responsive to treatment and widely used in constipation clinical trials and practice.

Marquis, P., De La Loge, C., Dubois, D., McDermott, A., & Chassany, O. (2005). Development and validation of the Patient Assessment of Constipation-Quality of Life questionnaire. Scandinavian Journal of Gastroenterology, 40(5), 540–551.

rome-iv-ibs-criteria gcsi gerd-hrql ibdq-short


Page's L Test

page trend test
page ordered alternatives test
Page L Testi — Sıralı Alternatifler

Page's L test is a nonparametric hypothesis test designed for repeated-measures (randomized complete block) designs in which the researcher has a specific, pre-stated ordering hypothesis across k ≥ 3 conditions. Introduced by Ellis Batten Page in 1963, it is more powerful than the Friedman test when the alternative hypothesis specifies a monotone trend rather than a general difference among conditions.

Repeated measures / randomized complete block

Page, E. B. (1963). Ordered hypotheses for multiple treatments: a significance test for linear ranks. Journal of the American Statistical Association, 58(301), 216–230.

Hollander, M. & Wolfe, D. A. (1999). Nonparametric Statistical Methods (2nd ed.). Wiley.

friedman-test jonckheere-terpstra-test kruskal-wallis-test wilcoxon-signed-rank-test kendall-w


PageRank

Google PageRank
Random Surfer Model
Link-Based Ranking
PageRank Merkeziliği

PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.

Iterative link-based centrality algorithm

Power iteration until score change < tolerance

Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.


Pain Anxiety Symptoms Scale

The Pain Anxiety Symptoms Scale (PASS) is a 20-item self-report instrument developed by Asmundson and colleagues in 1996 to measure anxiety symptoms specifically related to pain. The PASS captures fear of pain, avoidance behaviors, cognitive anxiety, and physiological anxiety responses that commonly accompany chronic pain and contribute to disability through fear-avoidance mechanisms.

Self-report scale measuring anxiety symptoms in response to pain

McWilliams, L.A., Asmundson, G.J., & Gauthier, N. (2006). Pain anxiety symptoms scale: Brief 20-item version (PASS-20). Journal of Pain, 7(7), 479-485.

Asmundson, G.J., Norton, P.J., & Norton, G.R. (1999). Beyond pain: The role of fear and avoidance in chronicity. Clinical Psychology Review, 19(1), 97-119.

McCracken, L.M., & Dhingra, L. (2002). A short version of the Pain Anxiety Symptoms Scale (PASS-20): Preliminary development and validity. Pain Research & Management, 7(1), 45-50.


Behavioral Pain Scale

Behavioral Pain Scale (BPS) for Critically Ill Patients

BPS
Behavioral assessment
ICU pain scale

The Behavioral Pain Scale (BPS), developed by Payen et al. in 2001, is a 12-point tool designed to assess pain in critically ill sedated or paralyzed patients who cannot communicate verbally. It evaluates facial expressions, upper limb movements, and ventilator compliance to quantify pain intensity despite sedation or neuromuscular blockade.

Payen, J. F., Bru, O., Bosson, J. L., et al. (2001). Assessing pain in critically ill sedated patients by using a behavioral pain scale. Critical Care Medicine, 29(12), 2258-2263.

Gelinas, C., Fillion, L., Puntillo, K. A., Viens, C., & Fortier, M. (2006). Validation of the Critical-Care Pain Observation Tool in adult patients. American Journal of Critical Care, 15(4), 420-427.


Numerical Rating Scale for Pain

Numerical Rating Scale for Pain Assessment

NRS
NRS-11
Numeric Pain Rating Scale
Pain Intensity Scale

Multiple researchers (Downie, Leatham, et al.)

The Numerical Rating Scale (NRS) is a simple, widely used tool for assessing subjective pain intensity in patients. Patients rate their pain on a scale from 0 to 10, where 0 represents no pain and 10 represents the worst pain imaginable. The NRS is one of the most frequently used pain assessment instruments in clinical practice due to its brevity, ease of administration, and strong psychometric properties across diverse patient populations.

Herr, K., Coyne, P. J., Key, T., et al. (2011). Pain assessment in the nonverbal patient: Position statement with clinical practice recommendations. Pain Management Nursing, 7(2), 44-52.

Williamson, A., & Hoggart, B. (2005). Pain: a review of three commonly used pain rating scales. Journal of Clinical Nursing, 14(7), 798-804.

cam-delirium-screening wound-assessment-bates-jensen nursing-sensitive-indicators patient-fall-risk-assessment


Pain Catastrophizing Scale

Michael J. Sullivan and Steven R. Bishop

The Pain Catastrophizing Scale (PCS) is a 13-item self-report questionnaire developed by Sullivan, Bishop, and Pivik in 1995 to measure catastrophic thinking about pain—the tendency to magnify pain threat, ruminate about pain, and feel helpless in response to pain. Elevated catastrophizing predicts worse pain outcomes and is a key treatment target in cognitive-behavioral pain management.

Self-report questionnaire measuring catastrophic thinking about pain

Sullivan, M.J., Bishop, S.R., & Pivik, J. (1995). The Pain Catastrophizing Scale: Development and validation. Psychological Assessment, 7(4), 524-532.

Sullivan, M.J.L., Thorn, B., Haythornthwaite, J.A., et al. (2001). Theoretical perspectives on the relation between catastrophizing and pain. Clinical Journal of Pain, 17(1), 52-64.

Osman, A., Barrios, F.X., Gutierrez, P.M., et al. (2000). The Pain Catastrophizing Scale: Further psychometric evaluation with adult samples. Journal of Behavioral Medicine, 23(4), 351-365.

pain-anxiety-symptoms-scale mcgill-pain-questionnaire pain-self-efficacy-questionnaire central-sensitization-inventory


Pain Self-Efficacy Questionnaire

The Pain Self-Efficacy Questionnaire (PSEQ) is a 10-item self-report instrument developed by Nicholas in 1989 to measure self-efficacy beliefs—a person's confidence in their ability to manage pain and function despite pain. Higher PSEQ scores predict better pain outcomes, less disability, and greater treatment success, making it a key measure in pain rehabilitation and psychological intervention research.

Self-report questionnaire measuring self-efficacy beliefs about managing chronic pain

Nicholas, M.K. (1989). Self-efficacy and chronic pain. The American Psychological Association Annual Convention, New Orleans, LA.

Nicholas, M.K., McArthur, G.D., Coulton, S., & Ashworth, M.A. (2007). Development and testing of a revised version of the Pain Self-Efficacy Questionnaire. In: Pain Medicine Clinical Update, 18, 5-7.

Anderson, K.O., Dowds, B.N., Pelletz, R.E., Edwards, W.T., & Peeters-Asdourian, C. (1995). Development and initial validation of a scale to measure self-efficacy beliefs in patients with chronic pain. Pain, 63(1), 77-84.

pain-catastrophizing-scale pain-anxiety-symptoms-scale roland-morris-disability chronic-pain-acceptance-questionnaire


Paired samples t-test

dependent t-test
matched pairs t-test
repeated measures t-test
within-subjects t-test

The paired samples t-test is a parametric hypothesis test that compares the means of two related measurements from the same subjects or matched pairs to determine whether the average difference is significantly different from zero. It leverages the dependency between observations to produce a more powerful test than its independent-samples counterpart.

Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.

independent-samples-t-test wilcoxon-signed-rank-test one-sample-t-test repeated-measures-anova mann-whitney-u-test


Paired t-test

dependent samples t-test
repeated measures t-test
matched-pairs t-test
eşleştirilmiş örneklem t-testi

The paired samples t-test is a parametric hypothesis test that compares two measurements taken on the same subjects — such as a before and after reading — to decide whether the average change differs from zero. It rests on the t-distribution introduced by Student (W. S. Gosset) in 1908 and works on the within-subject difference scores rather than the raw measurements.

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

independent-t-test wilcoxon-signed-rank repeated-measures-anova one-way-anova


Pairs Trading

Pairs Trading / Statistical Arbitrage Strategy

Çift Alım-Satım Stratejisi (Pairs Trading / Statistical Arbitrage)

Gatev, Goetzmann & Rouwenhorst (empirical rule); Vidyamurthy (quantitative framing)

Pairs trading is a quantitative trading strategy that takes a long-short position on two cointegrated assets when the gap (spread) between their prices shows mean reversion. It was popularised as a relative-value arbitrage rule by Gatev, Goetzmann and Rouwenhorst (2006) and framed quantitatively by Vidyamurthy (2004).

Cointegration-based mean-reversion trading strategy

Cointegrating regression (hedge ratio) + Ornstein-Uhlenbeck mean-reversion

Gatev, E., Goetzmann, W. N. & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. Review of Financial Studies, 19(3), 797–827.

Vidyamurthy, G. (2004). Pairs Trading: Quantitative Methods and Analysis. Wiley.

ols-regression har-rv-model risk-parity-model tail-risk-measures wavelet-finance


Paleomagnetic Analysis

Paleomagnetic analysis is the study of remnant magnetization in rocks and sediments to determine the direction and magnitude of the Earth's ancient magnetic field and to establish the ages and tectonic histories of crustal rocks. Formalized by Fisher (1953) and Butler (1992), paleomagnetism underpins plate tectonics plate reconstruction, magnetostratigraphic dating, and paleoclimate studies.

Analysis of remnant magnetization in rocks for chronology and tectonics

Fisher, R. A. (1953). Dispersion on a sphere. Proceedings of the Royal Society of London, 217(1130), 295-305.

Butler, R. F. (1992). Paleomagnetism: Magnetic domains to geological terranes. Blackwell Scientific Publications.


Paleomagnetism Analysis

paleomagnetic dating
magnetostratigraphy
paleomagnetic remanence

Paleomagnetism analysis is the study of ancient magnetic properties of rocks, measuring fossil magnetization to determine paleomagnetic field history and assign geological ages. Pioneered by Brunhes (1906) and systematized by Tauxe (2010), this method reveals geomagnetic reversals, polar wander paths, and paleomagnetic chronology independent of fossil biostratigraphy. Analysis integrates laboratory rock magnetism with field sampling to build high-resolution timescales and constrain plate motion.

Butler, R. F. (1992). Paleomagnetism: Magnetic Domains to Geologic Terranes. Blackwell Scientific Publications.

Cande, S. C., & Kent, D. V. (1995). Revised calibration of the geomagnetic polarity time scale for the Late Cretaceous and Cenozoic. Journal of Geophysical Research, 100(B4), 6093–6095.

Tauxe, L. (2010). Essentials of Paleomagnetism (1st ed.). University of California Press.

geochronological-dating stratigraphic-correlation basin-subsidence-analysis paleoenvironmental-analysis geochemical-analysis


Palliative Performance Scale

The Palliative Performance Scale (PPS) is an 11-point clinician-rated functional assessment tool for patients with advanced, life-limiting illness. Developed by Anderson and colleagues in 1996, it measures overall performance status from 100% (normal) to 0% (death), integrating five domains of functional decline. The PPS is widely used in palliative care, hospice, and oncology settings to guide treatment intensity, prognostication, and care planning.

Anderson, F., Downing, G. M., Hill, J., Casorso, L., & Lerch, N. (1996). Palliative Performance Scale: A new tool. J Palliat Care, 12(1), 5–11.

Glare, P. A., Semple, D., & Staquet, M. J. (2011). Palliative Performance Scale. In A. G. Liptak (Ed.), Palliative care: Core skills and clinical competencies (2nd ed., pp. 421–428). Saunders.

spiritual-wellbeing-scale mcgill-quality-of-life comfort-care-checklist patient-dignity-inventory facit-palliative


Palynology

Palynology — The Scientific Study of Pollen and Spores

pollen analysis
spore analysis
palynostratigraphy
aerobiology pollen study

Biological and geological trace analysis

Early 20th century (von Post 1916; formal discipline consolidated by mid-20th century)

Multiple contributors (Lennart von Post pioneered quantitative pollen analysis ~1916)

Palynology is the scientific study of pollen grains and plant spores — microscopic structures that are chemically resistant and preserve well in sediment, soil, peat, ice, and other matrices. In agronomy, palynology is applied to reconstruct past vegetation and land-use histories, monitor crop pollination dynamics, trace the botanical origin of honey, assess aeroallergen loads, and support plant breeding programmes. It bridges botany, ecology, archaeology, and environmental science.

Laboratory pipeline — morphological identification and quantitative counting

Pollen grains and spores (microscopic biological specimens from sediment, soil, air, or honey samples)

Faegri, K., & Iversen, J. (1989). Textbook of Pollen Analysis (4th ed.). Wiley.

Moore, P. D., Webb, J. A., & Collinson, M. E. (1991). Pollen Analysis (2nd ed.). Blackwell Scientific Publications.

carbon-dating sediment-core-analysis vegetation-mapping phenology aerobiology archaeobotany


PAM

PAM (Polygons Area Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Various authors in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Geometric aggregation via polygon area on equal-angle axes

Various authors (2010). Polygons Area Method for MCDM. Various journals

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


PAMSSEM-I

PAMSSEM-I (PAMSSEM I — Procédure d'Agrégation Multicritère) is a outranking multi-criteria decision-making (MCDM) method introduced by Martel, J. M., Aouni, B. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Martel, J. M., Aouni, B. (1996). Incorporating the decision-maker's preferences in the goal-programming model with fuzzy goal values. Journal of the Operational Research Society


PAMSSEM-II

PAMSSEM-II (PAMSSEM II — Total ranking variant) is a outranking multi-criteria decision-making (MCDM) method introduced by Martel, J. M., Aouni, B. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


Pan-Tompkins QRS Detection

QRS detection
R-peak detection
Heartbeat detection

The Pan-Tompkins algorithm is a real-time QRS detection method for electrocardiograms (ECGs) that identifies the R-peaks (ventricular depolarization) and QRS complexes from continuous cardiac waveforms. Published by Jiapu Pan and Willis Tompkins in 1985, it remains a standard reference for ECG processing and is widely implemented in clinical monitoring systems.

Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), 230-236.

Clifford, G. D., Azuaje, F., & McSharry, P. E. (2006). ECG statistics, noise, artifacts, and missing data. Advanced Methods and Tools for ECG Data Analysis, 1, 1-41.


Positive and Negative Affect Schedule

David Watson, Lee Anna Clark, and Auke Tellegen

The Positive and Negative Affect Schedule (PANAS) is a brief, efficient self-report measure of mood and emotional affect. Developed by Watson, Clark, and Tellegen in 1988, it assesses two independent dimensions: positive affect (enthusiasm, attentiveness, interest) and negative affect (distress, anxiety, anger). The 20-item standard version is one of the most widely used instruments for measuring emotion in research and clinical contexts.

Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063-1070.

Watson, D., & Clark, L. A. (1999). The PANAS-X: Manual for the positive and negative affect schedule—expanded form. University of Iowa.

hamilton-anxiety-rating-scale swls dass-21 hads ces-d


Pandemic Fatigue Scale

The Pandemic Fatigue Scale (PFS) measures psychological exhaustion and reduced motivation to maintain protective behaviors during prolonged pandemics. Developed by Restrepo and colleagues, it captures the phenomenon whereby individuals progressively abandon preventive measures (distancing, mask-wearing, testing) despite ongoing transmission risk, driven by 'fatigue' or loss of motivation rather than reduced threat perception. The PFS has become essential for monitoring behavioral adherence trends and explaining divergence between risk and protective behavior during multi-wave pandemics.

Restrepo, A., Pfeil, J., & Farias, M. (2021). Pandemic fatigue and the risk of SARS-CoV-2 transmission in a representative US sample. Nature Medicine, 27(6), 1093–1101.

Solis-Moreira, R., Arriaga, J., Gutierrez, R., & Loayza, M. (2021). Pandemic Fatigue Scale: Development and psychometric validation. Frontiers in Psychology, 12, 714606.


Pandemic Grief Scale

The Pandemic Grief Scale (PGS) is a brief screening instrument assessing grief reactions specific to death losses during COVID-19. Developed by Zisook and colleagues in 2021, it adapts the Inventory of Complicated Grief (ICG) items to pandemic bereavement contexts, measuring both typical grief responses and complicated grief symptoms. The PGS recognizes that pandemic deaths—often characterized by social isolation, restricted funeral rituals, and constrained grieving practices—create unique bereavement trajectories requiring tailored support.

Zisook, S., Iglewicz, A., Avanzato, M. R., Maglione, J., Glorioso, D., Zetumer, S., ... & Shuchter, S. R. (2021). Bereavement, complicated grief, and depression: A report from the experience registry of bereavement in adults. Journal of Affective Disorders Reports, 6, 100255.


Panel ADF Unit Root Test

Panel Augmented Dickey-Fuller Unit Root Test

Panel ADF test
IPS test
Im-Pesaran-Shin test
panel unit root test

Im, Pesaran & Shin (2003); Levin, Lin & Chu (2002)

The Panel Augmented Dickey-Fuller (Panel ADF) unit root test extends the classical ADF framework to panel datasets. By pooling information across cross-sectional units it achieves substantially higher power than single-series ADF tests, allowing researchers to determine whether time-series variables are stationary or integrated of order one before modelling long-run relationships.

Balanced or unbalanced panel (multiple cross-sections observed over time)

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.

Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.

augmented-dickey-fuller-unit-root-test panel-pp-unit-root-test panel-kpss-test panel-engle-granger-cointegration panel-johansen-cointegration panel-ardl-bounds-test


Panel AR model

panel autoregressive model
PAR model
AR model for panel data
panel AR(p)

The Panel AR model extends the classical univariate autoregressive model to panel data, capturing how each unit's own past values predict its current value while controlling for unobserved individual heterogeneity through fixed or random effects. It is foundational for modelling dynamic persistence in micro or macro panel datasets.

Autoregressive time-series model for panel data

Balanced or unbalanced panel (cross-sectional units observed over multiple time periods)

Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.

Arellano, M. (2003). Panel Data Econometrics. Oxford University Press.

panel-arma-model panel-arima-model panel-var-model panel-dynamic-panel-data-model arellano-bond-gmm-estimator fixed-effects-model


Panel ARDL Bounds Test

Panel Autoregressive Distributed Lag Bounds Testing Approach

Panel ARDL
Panel bounds testing
Panel ARDL cointegration
Panel PSS bounds test

The Panel ARDL Bounds Test extends the Pesaran, Shin and Smith (2001) bounds testing procedure to panel data, allowing researchers to test for long-run cointegrating relationships between variables without requiring all series to be integrated of the same order. It is widely used in macro-panel studies where variables may be I(0), I(1), or a mixture of both.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.

Pesaran, M. H., & Pesaran, B. (1997). Working with Microfit 4.0: Interactive Econometric Analysis. Oxford University Press.

panel-engle-granger-cointegration panel-johansen-cointegration panel-vecm panel-granger-causality nonlinear-ardl panel-nardl


Panel Arellano-Bond GMM

Panel Data Arellano-Bond Generalized Method of Moments Estimator

Arellano-Bond GMM
AB-GMM
difference GMM estimator
dynamic panel GMM

The Arellano-Bond GMM estimator addresses the two core problems of dynamic panel models — individual fixed effects correlated with the regressors, and the endogeneity introduced by a lagged dependent variable — by first-differencing to remove fixed effects and then using lagged levels of the dependent variable as internal instruments.

Balanced or unbalanced panel data with a lagged dependent variable

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.

arellano-bond-gmm-estimator panel-system-gmm panel-difference-gmm dynamic-panel-data-model panel-fixed-effects-model panel-random-effects-model


Panel ARIMA model

Panel Autoregressive Integrated Moving Average Model

Panel ARIMA
ARIMA for panel data
cross-sectional ARIMA
multi-unit ARIMA

Extension of Box-Jenkins ARIMA (Box & Jenkins, 1970) to panel settings; formalised in panel econometrics literature (Hsiao, 2003)

The Panel ARIMA model extends the classical Box-Jenkins ARIMA framework to panel data, fitting autoregressive integrated moving-average dynamics to multiple cross-sectional units observed over time. It accommodates unit-specific short-run dynamics and non-stationarity, making it suitable for forecasting and dynamic analysis when both cross-sectional and temporal dimensions are present.

Balanced or unbalanced panel (multiple units, multiple time periods)

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.

arima-model panel-data-analysis panel-var-model panel-ardl-bounds-test vector-autoregression panel-ar-model


Panel ARMA model

Panel Autoregressive Moving Average Model

Panel ARMA
ARMA panel model
panel autoregressive moving average
cross-sectional ARMA

Baltagi, Hsiao and related panel data literature

The Panel ARMA model extends the classical Autoregressive Moving Average (ARMA) framework to panel data, allowing each cross-sectional unit to carry an individual effect while the within-unit error dynamics follow an ARMA(p, q) process. It captures both autocorrelation and moving-average dependence in panel residuals, yielding efficient estimates when the error structure is correctly specified.

Balanced or unbalanced panel data with temporal ordering

Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). John Wiley & Sons.

panel-ar-model panel-ma-model arma-model panel-data-analysis panel-fixed-effects-model vector-autoregression


Panel-based Causal-Comparative Research

Panel-based Causal-Comparative Research Design

1950s–1980s (formalized across educational and social science methodology literature)

Building on causal-comparative tradition (John W. Best, 1959) extended to panel data structures in social and educational research

Panel-based causal-comparative research is a quantitative observational design that tracks the same sample of participants or units across multiple time points and then compares pre-existing groups to identify differences in outcomes. By combining the temporal depth of a panel structure with the group-contrast logic of causal-comparative (ex post facto) methodology, it allows researchers to examine how naturally occurring conditions — such as treatment exposure, policy changes, or demographic characteristics — relate to outcomes over time, without experimental random assignment.

Quantitative observational research design

Repeated measurements of the same units (individuals, schools, organizations) across at least two time points

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill.

Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.

causal-comparative-research longitudinal-research panel-study cohort-study fixed-effects-model difference-in-differences


Panel-based Cohort Research

panel cohort study
longitudinal panel cohort
cohort panel design
panel longitudinal study

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

Developed through convergence of epidemiological cohort methodology and social science panel survey traditions

Panel-based cohort research is a longitudinal observational design that follows a defined group of individuals — the cohort — across multiple repeated measurement waves, collecting structured quantitative data at each wave. It merges the epidemiological strength of cohort tracking (a group sharing a common characteristic or entry point) with the panel study convention of standardized, repeated-contact data collection. The design enables analysis of change over time within individuals while supporting causal inference about exposure-outcome relationships.

Quantitative longitudinal observational design

Repeated structured measurements on the same individuals over multiple time points

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

cohort-study panel-study longitudinal-research prospective-study repeated-measures-design survey-research


Panel-based Confirmatory Research

Panel-Based Confirmatory Research Design

confirmatory panel design
longitudinal confirmatory study
panel confirmatory analysis
PBCR

1960s–1980s (formalization of panel methods with confirmatory inference)

Multiple contributors; panel data analysis formalized by Yair Mundlak, Zvi Griliches, and Edwin Kuh in the 1960s–1970s; confirmatory integration developed across econometrics and SEM traditions

Panel-based confirmatory research combines the longitudinal power of panel data — repeated observations of the same units over time — with a pre-specified, hypothesis-driven analytic framework. Instead of exploring patterns post-hoc, the researcher commits to theoretical propositions before data collection and uses the panel structure to test causal or directional claims while controlling for unobserved time-invariant confounders. It is widely used in economics, sociology, epidemiology, and organizational research.

Quantitative longitudinal research design

Repeated-measures quantitative data from the same units (individuals, firms, countries) observed across two or more time points

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

longitudinal-research confirmatory-factor-analysis structural-equation-modeling cross-lagged-panel-model fixed-effects-model repeated-measures-design


Panel-based correlational research

Panel-based Correlational Research Design

panel correlational study
longitudinal correlational panel
panel survey research
repeated-measures correlational design

1970s–1980s (formal panel analysis methods)

Panel methodology systematized by economists and sociologists, notably Kessler & Greenberg (1981) and Cheng Hsiao (1986)

Panel-based correlational research follows the same individuals, organizations, or units across multiple time points and quantifies associations among variables within that longitudinal structure. Unlike a one-shot correlational survey, the panel design captures temporal ordering and within-unit change, enabling researchers to test whether earlier values of one variable predict later values of another while statistically controlling for stable individual differences.

Repeated measures from the same participants or units across two or more time points (panel/longitudinal survey data)

Kessler, R. C., & Greenberg, D. F. (1981). Linear Panel Analysis: Models of Quantitative Change. Academic Press.

longitudinal-survey cross-lagged-panel-model cohort-study time-series-analysis repeated-measures-anova correlational-research


Panel-based cross-sectional research

Panel-Based Cross-Sectional Research Design

panel cross-sectional survey
rotating panel cross-section
repeated cross-section panel
cross-sectional panel design

1940s–1960s (formalized in social survey methodology)

Panel survey methodology developed from large-scale government and social survey programs (e.g., University of Michigan Survey Research Center, 1940s–1950s)

Panel-based cross-sectional research draws repeated cross-sectional measurements from a pre-recruited standing panel rather than sampling fresh respondents each time. This hybrid design preserves the snapshot character of classic cross-sectional surveys while gaining speed, cost efficiency, and comparability across waves. It is widely used in social, health, and market research whenever population-level estimates are needed quickly and repeatedly without full longitudinal tracking of the same individuals.

Structured survey data; repeated or rotating samples drawn from a defined panel

Kasprzyk, D., Duncan, G., Kalton, G., & Singh, M. P. (Eds.). (1989). Panel Surveys. John Wiley & Sons.

Lynn, P. (Ed.). (2009). Methodology of Longitudinal Surveys. John Wiley & Sons.

longitudinal-research cross-sectional-survey cohort-study repeated-measures-design survey-research time-series-analysis