Posterior Predictive Model Checking (PPMC) is a Bayesian model checking method that compares the observed data to (plausible) future observations from the posterior predictive distribution. We propose an alternative to PPMC in the context of structural equation modeling, which we term the Poor Persons PPMC (PP-PPMC), for the situation wherein one cannot afford (or is unwilling) to draw samples from the full posterior. Using only byproducts of likelihood-based estimation (maximum likelihood estimate and information matrix), the PP-PPMC offers a natural method to handle parameter uncertainty in model fit assessment. In particular, a coupling relationship between the classical p-values from the model fit chi-square test and the predictive p-values from the PP-PPMC method is carefully examined, suggesting that PP-PPMC may offer an alternative, principled approach for model fit assessment. We also illustrate the flexibility of the PP-PPMC approach by applying it to case-influence diagnostics. [This paper was published in "Structural Equation Modeling" v23 n2 p206-220 2016.]