In this digital ITEMS module, Dr. Allison Ames and Aaron Myers discuss the most common Bayesian approach to model-data fit evaluation called Posterior Predictive Model Checking (PPMC). Specifically, drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model and violations of model-data fit have numerous adverse consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models is critical. The instructors review the conceptual foundation of Bayesian inference as well as PPMC and walk through the computational steps of PPMC using real-life data examples from simple linear regression and item response theory (IRT) analysis. They provide guidance for how to interpret PPMC results and discuss how to implement PPMC for other model(s) and data. The digital module contains sample data, SAS code, diagnostic quiz questions, data-based activities, curated resources, and a glossary.