Bayesian Structural Equation Modeling

Published

Journal Article (Chapter)

This chapter focuses on Bayesian structural equation modeling. Structural equation models (SEMs) with latent variables are routinely used in social science research, and are of increasing importance in biomedical applications. Standard practice in implementing SEMs relies on frequentist methods. The chapter provides a simple and concise description of an alternative Bayesian approach. A description of the Bayesian specification of SEMs, and an outline of a Gibbs sampling strategy for model fitting is also presented. Bayesian inferences are illustrated through an industrialization and democratization case study. The Bayesian approach has some distinct advantages, due to the availability of samples from the joint posterior distribution of the model parameters and latent variables, which are highlighted in the chapter. These posterior samples provide important information not contained in the measurement and structural parameters. © 2007 Elsevier B.V. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Palomo, J; Dunson, DB; Bollen, K

Published Date

  • December 1, 2007

Start / End Page

  • 163 - 188

Digital Object Identifier (DOI)

  • 10.1016/B978-044452044-9/50011-2

Citation Source

  • Scopus