Journal article

Choosing Priors in Bayesian Measurement Invariance Modeling: A Monte Carlo Simulation Study


Authors listPokropek, Artur; Schmidt, Peter; Davidov, Eldad

Publication year2020

Pages750-764

JournalStructural Equation Modeling: A Multidisciplinary Journal

Volume number27

Issue number5

ISSN1070-5511

eISSN1532-8007

Open access statusGreen

DOI Linkhttps://doi.org/10.1080/10705511.2019.1703708

PublisherTaylor 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 stylePokropek, 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 stylePokropek, 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 comparisonsFITMEASUREMENT INVARIANCEMonte Carlo simulation studySENSITIVITY-ANALYSIS

Last updated on 2025-10-06 at 11:07