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ERIC Number: EJ1281725
Record Type: Journal
Publication Date: 2021-Feb
Pages: 29
Abstractor: As Provided
ISSN: ISSN-0013-1644
Testing Measurement Invariance across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling
Wang, Yan; Kim, Eunsook; Ferron, John M.; Dedrick, Robert F.; Tan, Tony X.; Stark, Stephen
Educational and Psychological Measurement, v81 n1 p61-89 Feb 2021
Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A