Abstract
Structural equation models (SEMs) are extensively used in behavioral, social, and psychological research to model relationships between latent variables and observations. However, most models include redundant variables that are not important to relationships within the model. This study uses a Bayesian model selection procedure with mixture priors for SEMs to determine variables that have a meaningful relationship. The posterior probabilities for all possible models are calculated through the MCMC algorithm and the model with the largest probability is selected. The predictors of non-zero fixed-effect coefficients and/or non-zero random-effect variance components in the MCMC procedure are determined without the need for complex model selection criteria. The proposed methodologies are illustrated in a simulation and applied to a multidimensional longitudinal myopia data set in which the dimensionality of the parameter space is reduced and a more parsimonious model is selected.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.