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One catalogue of research methods — learn how each one works, when to use it, and what it can’t do.

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Entries are compiled from published sources for reference. Verifying the accuracy and suitability of any information for your own use remains your responsibility.

© 2026 ScholarGate · A research-method reference library
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Natural Sciences236
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MethodStatistics1,836AI & ML1,661Decision Sciences932Research Methods1,354Measurement1,745Causal & Evidence532Research Practice118
66 methods in Psychology · StatisticsClear
Methods at the intersection of your two filters.
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social psychology

Actor-Partner Interdependence Model

The Actor-Partner Interdependence Model (APIM), formalized by Kenny, Kashy, and Cook, is the standard framework for analyzing dyadic data in which two people's outcomes depend on both their own and their partner's characteristics. For each member of a dyad, the model estimates an actor effect -- the influence of a pers

1 source2006
psychometrics

Bayesian Confirmatory Factor Analysis

Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parame

2 sources2007
psychometrics

Bayesian Construct Validity

Bayesian construct validity assessment uses Bayesian confirmatory factor analysis and related Bayesian structural equation models to evaluate whether a scale or test measures the intended latent construct. It yields full posterior distributions for factor loadings, structural coefficients, and model-fit indices rather

2 sources1955
psychometrics

Bayesian Convergent Validity

Bayesian convergent validity applies Bayesian statistical inference to assess whether different measures of the same construct converge as theory predicts. Rather than a single-point correlation estimate, it yields a full posterior distribution over the convergent correlation, enabling probability statements about the

2 sources2000
psychometrics

Bayesian Cronbach's alpha

Bayesian Cronbach's alpha applies Bayesian inference to estimate the classical internal-consistency coefficient, yielding a full posterior distribution over alpha rather than a single point estimate. This allows researchers to quantify uncertainty with credible intervals and incorporate prior knowledge, making reliabil

2 sources2011
psychometrics

Bayesian Differential Item Functioning

Bayesian differential item functioning analysis detects whether a test item behaves differently across demographic or cultural groups — such as males vs. females — after accounting for the underlying ability or trait being measured. It applies Bayesian IRT estimation to obtain posterior distributions of item parameters

2 sources1990
psychometrics

Bayesian Discriminant Validity

Bayesian discriminant validity assessment evaluates whether two theoretically distinct latent constructs are empirically separable, using posterior distributions and credible intervals rather than single-point null-hypothesis tests. It is applied within Bayesian confirmatory factor analysis or via the Bayesian heterotr

2 sources2020
psychometrics

Bayesian EFA

Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factor

2 sources2004
psychometrics

Bayesian Item Analysis

Bayesian item analysis applies Bayesian inference to estimate item-level statistics — difficulty, discrimination, and distractor effectiveness — by combining observed response data with prior knowledge. It produces full posterior distributions over item parameters rather than single point estimates, providing richer un

2 sources1990
psychometrics

Bayesian McDonald's omega

Bayesian McDonald's omega applies Bayesian statistical estimation to the omega reliability coefficient, yielding a full posterior distribution over omega rather than a single point estimate. This provides credible intervals and probabilistic uncertainty quantification for the reliability of a composite or scale score,

2 sources1999
psychometrics

Bayesian Measurement Invariance

Bayesian measurement invariance testing evaluates whether a scale's factor loadings and item intercepts are equivalent across groups, using a Bayesian framework that allows parameters to deviate from strict equality by a small, probabilistically specified amount rather than imposing an exact constraint.

2 sources2013
psychometrics

Bayesian Scale Development

Bayesian scale development applies Bayesian statistical inference to the construction and evaluation of psychometric scales. Rather than relying on single point estimates of item and person parameters, it produces full posterior distributions that quantify uncertainty, incorporate prior knowledge, and support principle

2 sources1990
clinical psychology

Cambridge Depersonalisation Scale

The CDS is a 29-item self-report measure of depersonalisation and derealisation experiences, developed by Sierra and Berrios in 2000. It is the most widely used instrument for assessing dissociative symptom severity in both clinical and research settings, valuable for identifying depersonalisation disorder, monitoring

1 source2000
political psychology

Candidate Evaluation Model

A candidate evaluation model represents how voters form overall assessments of political candidates as a latent function of perceived traits (competence, leadership, integrity, empathy), partisanship, issue proximity, and affect. It spans the trait-based factor models of Kinder et al. (1980) and the online-processing t

2 sources1995
psychometrics

CAT Cronbach's Alpha

Cronbach's alpha applied to computerized adaptive test (CAT) data estimates internal consistency reliability under the special condition that different examinees receive different subsets of items. Because the classic formula assumes every respondent answers the same items, its direct application to CAT data violates c

2 sources1984
psychometrics

CAT Test-Retest Reliability

Computerized adaptive test (CAT) test-retest reliability quantifies the consistency of ability estimates obtained when the same examinees complete a CAT on two separate occasions. Because adaptive algorithms tailor each examinee's item set individually, traditional reliability frameworks must be adapted to account for

2 sources1970
psychometrics

CFA — Scale Validation

Confirmatory factor analysis is a measurement modelling technique that tests whether a hypothesised factor structure — typically derived from theory or an earlier exploratory analysis — fits observed data from a new sample. Developed by Karl Jöreskog in 1969, it became the dominant tool for validating psychological sca

2 sources1969
psychotherapy research

Common Factors Questionnaire

The Common Factors Questionnaire (CFQ) is a structured client-report measure that quantifies the client's perception of therapeutic factors deemed common to effective psychotherapy across all modalities—including alliance, therapist empathy, client agency, goal clarity, and emotional expression. Based on Lambert's cont

2 sources1992
psychometrics

Computerized Adaptive Testing

Computerized Adaptive Testing (CAT) is an individualized assessment methodology in which a computer algorithm selects successive test items based on a running estimate of each examinee's latent ability. Grounded in Item Response Theory, CAT dynamically tailors the item sequence so that each question is optimally inform

1 source2000
psychometrics

Confirmatory factor analysis

Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix

2 sources1969
psychometrics

Confirmatory Factor Analysis for Scales

Confirmatory Factor Analysis (CFA) is a statistical method for testing whether a hypothesized factorial structure fits empirical data. Developed by Karl G. Jöreskog in 1969, CFA is the standard approach for validating psychometric scales by evaluating whether items load onto theoretically specified latent factors as ex

3 sources1969
psychometrics

DIF Analysis

Differential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability respond differently to a test item. First formalised by Holland and Thayer in 1988 via the Mantel-Haenszel procedure, it is the principal too

2 sources1988
psychometrics

EFA for Scale Development

Exploratory Factor Analysis for Scale Development is the psychometric application of EFA in which an item pool is administered and the resulting response data are analysed to discover the latent factor structure underlying the items. Originating with Spearman's (1904) factor theory and formalised for applied scale cons

2 sources1904
psychometrics

Exploratory Structural Equation Modeling

Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load

3 sources2009
psychometrics

Factor Analysis for Scale Development

Exploratory factor analysis (EFA) is a statistical method for discovering the underlying dimensional structure of a set of items or variables. Pioneered by Louis Thurstone in the mid-20th century, EFA is widely used to develop and validate psychometric scales by identifying groups of items that correlate together, ther

3 sources1947
psychometrics

Fuzzy ANOVA

Fuzzy ANOVA extends classical analysis of variance to fuzzy data where observations and group memberships are imprecise or uncertain. Developed by Viertl and others, Fuzzy ANOVA tests whether fuzzy-valued groups differ significantly while accounting for inherent measurement uncertainty.

3 sources2011
political psychology

Ideological Constraint Analysis

Ideological constraint analysis measures the degree to which an individual's or a public's political attitudes hang together in a coherent, predictable structure, the extent to which knowing a person's position on one issue lets you predict their positions on others. Introduced by Converse (1964) as the defining featur

2 sources1964
psychometrics

Interrater Reliability

Interrater reliability quantifies the degree to which two or more independent raters produce consistent scores when evaluating the same individuals or products. The family encompasses Cohen's kappa, introduced in 1960 for categorical judgments, and the Intraclass Correlation Coefficient (ICC) for continuous ratings, to

2 sources1960
psychometrics

Latent Profile Analysis

Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for ap

1 source2010
psychometrics

Latent Transition Analysis

Latent Transition Analysis (LTA) is a method for studying transitions between latent classes over time, developed by Collins and Lanza (2010). LTA combines latent class analysis (grouping individuals into classes) with Markovian transition models to understand how people move between qualitatively distinct states acros

3 sources2002
psychometrics

Longitudinal CFA

Longitudinal confirmatory factor analysis (longitudinal CFA) applies a theoretically specified measurement model to data collected at two or more time points. Its primary purpose is to verify that a scale measures the same latent construct in the same way over time — a prerequisite for drawing valid conclusions about c

2 sources1970
psychometrics

Longitudinal Cronbach's Alpha

Longitudinal Cronbach's alpha assesses the internal consistency reliability of a scale at each wave of a repeated-measures study and examines whether that reliability remains stable across time. It is an essential step in longitudinal scale validation, ensuring that a scale measures its construct with consistent precis

2 sources1951
psychometrics

Longitudinal EFA

Longitudinal EFA applies exploratory factor analysis separately at each measurement occasion — or jointly across occasions — to discover whether the same latent factor structure emerges over time and whether factor loadings remain stable across waves. It is the foundational data-driven approach for examining structural

2 sources1970
psychometrics

Multi-group confirmatory factor analysis

Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified.

2 sources1971
psychometrics

Multi-group Cronbach's alpha

Multi-group Cronbach's alpha estimates and compares the internal consistency reliability of a scale separately within each of two or more defined subgroups. It is used in cross-cultural, demographic, and comparative psychometric research to establish that a scale measures its construct with equivalent precision across

2 sources1951
psychometrics

Multi-group EFA

Multi-group exploratory factor analysis estimates the latent factor structure of a set of items separately within each of two or more groups and then examines whether the discovered structures are consistent across groups. It is used to explore dimensionality before imposing invariance constraints, and to diagnose grou

2 sources1981
psychometrics

Multilevel CFA

Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the

2 sources1994
psychometrics

Multilevel Content Validity

Multilevel content validity extends the classical content validity framework to settings where items, raters, or respondents are nested within hierarchical structures — such as students within schools, patients within clinics, or items rated by panels from distinct cultural or professional groups. It ensures that scale

2 sources1975
psychometrics

Multilevel Convergent Validity

Multilevel convergent validity evaluates whether items or scales intended to measure the same construct show coherent, strong associations at each level of a nested data structure — within individuals, within groups, and between groups. It extends classical convergent validity from single-level measurement models into

2 sources2005
psychometrics

Multilevel Differential Item Functioning

Multilevel DIF analysis detects whether individual test or survey items function differently across groups when respondents are clustered within higher-level units — such as students nested in schools, employees in organizations, or patients in clinics. By accounting for hierarchical data structure, it separates genuin

2 sources2001
psychometrics

Multilevel Discriminant Validity

Multilevel discriminant validity evaluates whether theoretically distinct constructs are empirically separable when data are nested within higher-level units such as teams, schools, or organizations. It extends single-level discriminant validity checks into a multilevel confirmatory factor analysis framework, verifying

2 sources2005
psychometrics

Multilevel EFA

Multilevel exploratory factor analysis uncovers latent factor structures simultaneously at two or more levels of a data hierarchy — for example, both within individuals and between groups — without imposing a fixed structure in advance. It is essential whenever survey or test items are collected from respondents nested

2 sources1994
psychometrics

Multilevel Generalizability Theory

Multilevel generalizability theory extends classical G-theory to measurement designs where observations are nested within higher-level units — for example, items nested within raters, or students nested within classrooms. It decomposes score variance into components attributable to persons, facets, and their interactio

2 sources1990
psychometrics

Multilevel McDonald's omega

Multilevel McDonald's omega estimates reliability at two distinct levels — within groups and between groups — for scales administered to individuals nested in clusters such as classrooms, teams, or organizations. It accounts for the non-independence induced by grouping and avoids the bias that single-level omega produc

2 sources1999
psychometrics

Multilevel Measurement Invariance

Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organi

2 sources2000
psychometrics

Multilevel nomological validity

Multilevel nomological validity evaluates whether a psychological construct and its network of theoretical relationships hold consistently across multiple levels of analysis — such as individual, team, and organization. It extends classical construct validation to nested data structures, ensuring that a measure means t

2 sources2005
psychometrics

Multilevel Rasch Model

The multilevel Rasch model extends the standard Rasch model to data with a nested structure — for example, students within classrooms within schools — by embedding person ability parameters inside a hierarchical linear model. It yields item difficulty estimates on a logit scale while simultaneously partitioning person-

2 sources1997
psychometrics

Multilevel Reliability Analysis

Multilevel reliability analysis estimates the internal consistency of scale scores separately at the within-group (individual) and between-group (cluster) levels. It corrects the bias that arises when ordinary alpha or omega is applied to hierarchically nested data, such as employees within organizations or students wi

2 sources2014
psychometrics

Multilevel Scale Development

Multilevel scale development constructs and validates measurement instruments for data collected from individuals nested within higher-level units such as classrooms, organizations, or clinics. It partitions item variance into within-group and between-group components, ensuring that reliability and factor structure are

2 sources1990
psychometrics

Multilevel Test-Retest Reliability

Multilevel test-retest reliability estimates how consistently a measurement instrument produces the same scores across repeated administrations when observations are nested within higher-level units — such as patients within clinics or students within classrooms. It partitions total score variance across levels using i

2 sources1979
psychometrics

Multiple Factor Analysis

Multiple Factor Analysis (MFA) is a dimension reduction technique developed by Escofier and Pagès (1985) for analyzing multiple groups of variables measured on the same observations. MFA balances the influence of each variable group to provide a unified view of how observations relate across multiple perspectives.

3 sources1985
psychometrics

Ordinal 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.

2 sources1984
psychometrics

Ordinal Cronbach's Alpha

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

2 sources2007
psychometrics

Ordinal EFA

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 or

2 sources1978
psychometrics

Partial Least Squares Structural Equation Modeling

PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex m

3 sources1985
political psychology

Political Ideology Scaling

Political ideology scaling estimates actors' positions on one or more latent ideological dimensions from their observed choices, most often legislators' roll-call votes, but also survey responses and donations. The dominant methods are Poole and Rosenthal's NOMINATE (1985) and the Bayesian item-response-theory (IRT) ap

2 sources1985
psychometrics

Polytomous Confirmatory Factor Analysis

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

2 sources1984
psychometrics

Polytomous EFA

Polytomous exploratory factor analysis extends standard EFA to ordered categorical (Likert-type) response data by replacing the Pearson correlation matrix with a polychoric correlation matrix. It recovers the latent continuous variable that each polytomous item is assumed to reflect, yielding more accurate factor loadi

2 sources1978
political psychology

Post-Materialism Index

The Post-Materialism Index, developed by Ronald Inglehart (1971), classifies individuals as materialist, postmaterialist, or mixed based on the priority they assign to physical and economic security versus self-expression, belonging, and quality of life. It operationalizes Inglehart's silent-revolution thesis that pros

2 sources1971
psychometrics

Robust Cronbach's Alpha

Robust Cronbach's alpha adapts the classical internal consistency coefficient to data that violate the assumption of multivariate normality or contain influential outliers. By replacing the conventional sample covariance matrix with a robust counterpart, it yields a reliability estimate that is resistant to distortion

2 sources2002