Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling.

ABSTRACT: 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 invaria

SUBMITTER: Wang Y 

PROVIDER: S-EPMC7797957 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature