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

Autor/inn/en	Wang, Yan; Kim, Eunsook; Ferron, John M.; Dedrick, Robert F.; Tan, Tony X.; Stark, Stephen
Titel	Testing Measurement Invariance across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling
Quelle	In: Educational and Psychological Measurement, 81 (2021) 1, S.61-89 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Yan) ORCID (Kim, Eunsook)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164420925122
Schlagwörter	Role; Error of Measurement; Monte Carlo Methods; Models; Comparative Analysis; Goodness of Fit; Evaluation Methods + Suchen Sie Ihr Suchwort? Rollen; Messfehler; Monte-Carlo-Methode; Analogiemodell
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 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01