In this commentary, Englehard and Perkins remark that Maydeu-Olivares has presented a framework for evaluating the goodness of model-data fit for item response theory (IRT) models and correctly points out that overall goodness-of-fit evaluations of IRT models and data are not generally explored within most applications in educational and psychological measurement. Maydeu-Olivares argues that overall goodness-of-fit (GOF) is not typically evaluated in psychometric applications because accurate p-values are not known for the commonly used GOF statistics, such as the Pearson (Chi[superscript 2]) and likelihood ratio (G[superscript 2]) statistics. Maydeu-Olivares presents new statistics for model-data fit with accurate "p"-values that are called limited-information statistics, and proposes using the information in the lower-level marginals of the contingency tables (univariate and bivariate) to provide more accurate GOF statistics. This strategy is designed to minimize the effects of sparse cells when a large number of test items are being evaluated for overall goodness of model-data fit. Englehard and Perkins limit their comments to some historical and philosophical aspects of model-data fit from the perspective of invariant measurement (Engelhard, 2013; Millsap, 2011). Their aim is to selectively consider some of the major points made by Maydeu-Olivares from the perspective of invariant measurement. According to Maydeu-Olivares, "The aim of this article is to provide a comprehensive framework for goodness of fit assessment in IRT modeling" (p. 72), however, these authors believe that he has offered a thoughtful argument for persuading applied measurement theorists to begin adding overall model-data fit indices based on limited-information statistics to the assessment of model-data fit. Maydeu-Olivares has raised many significant questions and issues that have not been fully discussed within the context of measurement theories based on IRT models. In summary, these authors find the program of research on omnibus GOF statistics undertaken by Maydeu-Olivares to be thought-provoking.