To ensure meaningful comparison of test scores across groups or time, measurement
invariance (ie, invariance of the general factor structure and the values of the measurement …

Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing
studies. However, there is a lack of guidance on the appropriate method to deal with ordinal …

Evaluations of measurement invariance provide essential construct validity evidence—a
prerequisite for seeking meaning in psychological and educational research and ensuring …

This simulation study investigated the empirical Type I error rates of using the maximum
likelihood estimation method and Pearson covariance matrix for multi-group confirmatory …

This Monte Carlo study investigates the beneficiary effect of including auxiliary variables
during estimation of confirmatory factor analysis models with multiple imputation …

Measurement invariance research has focused on identifying biases in test indicators
measuring a latent trait across two or more groups. However, relatively little attention has …

The evaluation of measurement invariance is an important step in establishing the validity
and comparability of measurements across individuals. Most commonly, measurement …

To date, no effective empirical method has been available to identify a truly invariant
reference variable (RV) in testing measurement invariance under a multiple-group …

Multiple-indicators multiple-causes (MIMIC) modeling is often used to test a latent group
mean difference while assuming the equivalence of factor loadings and intercepts over …

Multiple imputation (MI) is one of the recommended techniques for handling missing data in
ordinal factor analysis models. However, methods for computing MI-based fit indices under …