Publication: Choosing priors in bayesian measurement invariance modeling: A Monte Carlo Simulation study
Choosing priors in bayesian measurement invariance modeling: A Monte Carlo Simulation study
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Pokropek, A., Schmidt, P., & Davidov, E. (2020). Choosing priors in bayesian measurement invariance modeling: A Monte Carlo Simulation study. Structural Equation Modeling, 27(5), 750–764. https://doi.org/10.1080/10705511.2019.1703708
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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
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- Pokropek, Artur
- Schmidt, Peter
- Davidov, Eldad
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Pokropek, A., Schmidt, P., & Davidov, E. (2020). Choosing priors in bayesian measurement invariance modeling: A Monte Carlo Simulation study. Structural Equation Modeling, 27(5), 750–764. https://doi.org/10.1080/10705511.2019.1703708
