Journal article
Authors list: Pokropek, Artur; Schmidt, Peter; Davidov, Eldad
Publication year: 2020
Pages: 750-764
Journal: Structural Equation Modeling: A Multidisciplinary Journal
Volume number: 27
Issue number: 5
ISSN: 1070-5511
eISSN: 1532-8007
Open access status: Green
DOI Link: https://doi.org/10.1080/10705511.2019.1703708
Publisher: Taylor and Francis Group
Abstract:
Multi-group Bayesian structural equation modeling (MG-BSEM) gained considerable attention among substantive researchers investigating cross-group differences and methodologists exploring challenges in measurement invariance testing. MG-BSEM allows for greater flexibility by applying elastic rather than strict equality constraints on item parameters across groups. This, however, requires a specification of user-defined prior variances for cross-group differences in item parameters. Although prior selection in general Bayesian settings is well-studied, guidelines with respect to tuning the normal prior variances in MG-BSEM approximate measurement invariance (AMI) analysis are still largely missing. In a Monte Carlo simulation study we find that correctly specifying prior variances results in more precise credibility intervals (CI) and posterior standard deviations, while prior misspecification has little influence on point estimates. We compared the BIC, DIC, and PPP fit measures and found in our simulation scenarios that the DIC measure was most effective, when a proper threshold for model selection was applied.
Citation Styles
Harvard Citation style: Pokropek, A., Schmidt, P. and Davidov, E. (2020) Choosing Priors in Bayesian Measurement Invariance Modeling: A Monte Carlo Simulation Study, Structural Equation Modeling: A Multidisciplinary Journal, 27(5), pp. 750-764. https://doi.org/10.1080/10705511.2019.1703708
APA Citation style: Pokropek, A., Schmidt, P., & Davidov, E. (2020). Choosing Priors in Bayesian Measurement Invariance Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal. 27(5), 750-764. https://doi.org/10.1080/10705511.2019.1703708
Keywords
Bayesian structural equation modeling (BSEM); cross-group comparisons; FIT; MEASUREMENT INVARIANCE; Monte Carlo simulation study; SENSITIVITY-ANALYSIS