Adapting Fit Indices for Bayesian Structural Equation Modeling: Comparison to Maximum Likelihood

Mauricio Garnier-Villarreal*, Terrence D. Jorgensen

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


In a frequentist framework, the exact fit of a structural equation model (SEM) is typically evaluated with the chi-square test and at least one index of approximate fit. Current Bayesian SEM (BSEM) software provides one measure of overall fit: the posterior predictive p value (PPP±2). Because of the noted limitations of PPPχ2, common practice for evaluating Bayesian model fit instead focuses on model comparison, using information criteria or Bayes factors. Fit indices developed under maximumlikelihood estimation have not been incorporated into software for BSEM. We propose adapting 7 chi-square-based approximate fit indices for BSEM, using a Bayesian analog of the chi-square model-fit statistic. Simulation results show that the sampling distributions of the posterior means of these fit indices are similar to their frequentist counterparts across sample sizes, model types, and levels of misspecification when BSEMs are estimated with noninformative priors. The proposed fit indices therefore allow overall model-fit evaluation using familiar metrics of the original indices, with an accompanying interval to quantify their uncertainty. Illustrative examples with real data raise some important issues about the proposed fit indices' application to models specified with informative priors, when Bayesian and frequentist estimation methods might not yield similar results.

Original languageEnglish
Pages (from-to)46-70
JournalPsychological Methods
Issue number1
Publication statusAccepted/In press - 1 Jan 2019
Externally publishedYes


  • Bayesian
  • BSEM
  • Fit indices
  • Model fit
  • Structural equation modeling


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