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A generalized dimensionality discrepancy measure is introduced to facilitate a critique of dimensionality assumptions in multidimensional item response models. Connections between dimensionality and local independence motivate the development of the discrepancy measure from a conditional covariance theory perspective. A simulation study and a real-data analysis demonstrate the utility of the discrepancy measure's application at multiple levels of analysis in a posterior predictive model checking framework.