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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 източника2007
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 източника1955
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 източника2000
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 източника2011
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 източника1990
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 източника2020
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 източника2004
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 източника1990
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 източника1999
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 източника2013
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 източника1990
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 източник2000
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 източника1984
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 източника1970
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 източника1969
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 източника1992
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 източник2000
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 източника1969
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 източника1969
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 източника1988
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 източника1904
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 източника2009
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 източника1947
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 източника2011
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 източника1960
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 източник2010
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 източника2002
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 източника1970
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 източника1951
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 източника1970
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 източника1971
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 източника1951
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 източника1981
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 източника1994
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 източника1975
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 източника2005
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 източника2001
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 източника2005
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 източника1994
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 източника1990
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 източника1999
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 източника2000
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 източника2005
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 източника1997
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 източника2014
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 източника1990
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 източника1979
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 източника1985
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 източника1984
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 източника2007
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 източника1978
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 източника1985
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 източника1984
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 източника1978
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 източника2002
psychometrics

Robust Exploratory Factor Analysis

Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such

2 източника2000
psychometrics

Robust Test-Retest Reliability

Robust test-retest reliability quantifies how consistently a measure ranks or scores the same individuals across two occasions while protecting the estimate from distortion by outliers and non-normal score distributions. It replaces or supplements classical Pearson-based correlation and standard ICC formulas with robus

2 източника1990
psychometrics

Short-Form CFA

Short-form confirmatory factor analysis applies CFA to a reduced subset of items drawn from a longer validated scale, testing whether the abbreviated version preserves the original factor structure with acceptable model fit and reliability. It is a standard step in short-form scale development and validation.

2 източника1990
psychometrics

Short-form Cronbach's alpha

Short-form Cronbach's alpha quantifies the internal consistency reliability of an abbreviated version of a psychological scale. It applies Cronbach's alpha formula to a reduced item set, verifying that the shortened instrument retains sufficient reliability to support valid score interpretation in research and applied

2 източника1951
psychometrics

Test-Retest Reliability

Test-retest reliability quantifies the temporal consistency of a measure by correlating scores obtained from the same participants on two separate occasions. It is a cornerstone of psychometric validation, directly indicating whether a scale or instrument yields stable scores when the underlying construct has not chang

2 източника1904