Testing for measurement invariance with respect to an ordinal variable

DOI10.1007/s11336-013-9376-7zbMath1303.62106OpenAlexW2149618113WikidataQ43496453 ScholiaQ43496453MaRDI QIDQ487594

Edgar C. Merkle, Achim Zeileis, Jin Yan Fan

Publication date: 22 January 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10419/73875

zbMATH Keywords

stochastic processfactor analysisordinal datameasurement invariance

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to psychology (62P15)

Related Items (8)

Score-based tests of differential item functioning via pairwise maximum likelihood estimation ⋮ Heteroscedastic latent trait models for dichotomous data ⋮ Score‐based measurement invariance checks for Bayesian maximum‐a‐posteriori estimates in item response theory ⋮ Power analysis for the Wald, LR, score, and gradient tests in a marginal maximum likelihood framework: applications in IRT ⋮ Rasch trees: a new method for detecting differential item functioning in the Rasch model ⋮ Network trees: a method for recursively partitioning covariance structures ⋮ A note on the structural change test in highly parameterized psychometric models ⋮ Computation and application of generalized linear mixed model derivatives using \textit{lme4}

Uses Software

• R
• mvtnorm
• sandwich
• strucchange
• lavaan

Cites Work

• Unnamed Item
• A scaled difference chi-square test statistic for moment structure analysis
• Rasch trees: a new method for detecting differential item functioning in the Rasch model
• Implementing a class of structural change tests: an econometric computing approach
• Tests of measurement invariance without subgroups: a generalization of classical methods
• Fitting multivariage normal finite mixtures subject to structural equation modeling
• Generalized M‐fluctuation tests for parameter instability
• Generalized Maximally Selected Statistics
• Mean and Covariance Structure Analysis: Theoretical and Practical Improvements
• Tests For Constancy Of Model Parameters Over Time
• A new look at the statistical model identification

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