Bayesian item response theory (IRT) modeling stages include (a) specifying the IRT likelihood model, (b) specifying the parameter prior distributions, (c) obtaining the posterior distribution, and (d) making appropriate inferences. The latter stage, and the focus of this research, includes model criticism. Choice of priors with the posterior predictive checks (PPC) model-checking method requires more attention. The objective of this research is to investigate the extent of the effect of prior specification on the conclusions drawn from the PPC method. Findings indicated that the choice of discrepancy measure is an important factor in the overall success of the method, and that different discrepancy measures are affected more than others by prior specification. The use of percent correct as a discrepancy statistic was ineffective regardless of prior specification or type of misfit. Recommendations and suggestions for future research are provided.