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Item response theory (IRT) is a popular modeling paradigm for measuringsubject latent traits and item properties according to discrete responses intests or questionnaires. There are very limited discussions on heterogeneitypattern detection for both items and individuals. In this paper, we introduce anonparametric Bayesian approach for clustering items and individualssimultaneously under the Rasch model. Specifically, our proposed method isbased on the mixture of finite mixtures (MFM) model. MFM obtains the number ofclusters and the clustering configurations for both items and individualssimultaneously. The performance of parameters estimation and parametersclustering under the MFM Rasch model is evaluated by simulation studies, and areal date set is applied to illustrate the MFM Rasch modeling.

A Nonparametric Bayesian Item Response Modeling Approach for Clustering Items and Individuals Simultaneously