摘要 : 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... 展开 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 tit: the posterior predictive p value (PPP,(2). Because of the noted limitations of PPP chi(2), common practice for evaluating Bayesian model fit instead 'focuses on model comparison, using information criteria or Hayes 'factors. Fit indices developed under maximum likelihood 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 tit indices are, similar to their frequentist counterparts across sample, sizes, model types, and levels of misspe,cification 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. 收起