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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Choosing priors in bayesian measurement invariance modeling: A Monte Carlo Simulation study
Organization Unit
Authors
  • Artur Pokropek
  • Peter Schmidt
  • Eldad Davidov
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Structural Equation Modeling
Publisher Taylor & Francis
Geographical Reach international
ISSN 1070-5511
Volume 27
Number 5
Page Range 750 - 764
Date 2020
Abstract Text 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.
Digital Object Identifier 10.1080/10705511.2019.1703708
Other Identification Number merlin-id:19005
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Keywords Measurement invariance, Bayesian structural equation modeling (BSEM), cross-group comparisons, Monte Carlo simulation study
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis available online: http://wwww.tandfonline.com/10.1080/10705511.2019.1703708