A Comparison of FIML- versus Multiple-imputation-based methods to test measurement invariance with incomplete ordinal variables. Issue 4 (4th July 2021)

• Record Type: Journal Article
• Authors: Liu, Yu; Sriutaisuk, Suppanut
• Is Part Of: Structural equation modeling. Volume 28:Issue 4(2021)
• Page Start: 590
• Page End: 608
• Publication Date: 2021-07-04
• Subjects: Missing data -- ordinal data -- measurement invariance -- full information maximum likelihood -- multiple imputation; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1876520
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: To ensure meaningful comparison of test scores across groups or time, measurement invariance (i.e., invariance of the general factor structure and the values of the measurement parameters) across groups or time must be examined. However, many empirical examinations of measurement invariance of psychological/educational questionnaires need to address two issues: Using the appropriate model for ordinal variables (e.g., Likert scale items), and handling missing data. In two Monte Carlo simulations, this study examined the performance of one full-information-maximum-likelihood-based method and five multiple-imputation-based methods to obtain tests of measurement invariance across groups for ordinal variables that have missing data. Our results indicate that the full-information-maximum-likelihood-based method and one of the multiple-imputation-based methods generally have better performance than the other examined methods, though they also have their own limitations.