Generalized Bayesian Method for Diagnostic Classification Models

This study extends the loss-function-based parameter estimation method for diagnostic classification models proposed by Ma et al. (2023, Psychometrika) to consider prior knowledge and uncertainty of sampling. To this end, we integrate the loss-function-based estimation method with the general Bayesian method. We establish the consistency of attribute mastery patterns of the proposed general Bayesian method. The proposed general Bayesian method is compared in a simulation study and found to be superior to the previous nonparametric diagnostic classification method―a special case of the loss function-based method. Moreover, the proposed method is applied to real data and compared with previous parametric and nonparametric estimation methods. Finally, practical guidelines for the proposed method and future research directions are discussed.