Despite its importance to structural equation modeling, model evaluation remains underdeveloped in the Bayesian SEM framework. Posterior predictive p-values (PPP) and deviance information criteria (DIC) are now available in popular software for Bayesian model evaluation, but they remain under-utilized. This is largely due to the lack of recommendations and guidelines for their use. To address this problem, PPP and DIC are evaluated in a series of Monte Carlo simulation studies. The results from these studies show that PPP and DIC are influenced by severity of model misspecification, sample size, model size, and choice of prior. It was also found that the cut-offs PPP<0.10 and [Delta]>7 work best in the conditions and models tested here to maintain false detection rates and misspecified model selection rates, respectively, at 0.05. The recommendations provided in this study will help researchers evaluate their models in a Bayesian SEM analysis, and set the stage for future development and evaluation of PPP, DIC, and other Bayesian SEM fit indices. [This paper was published in "Structural Equation Modeling."]