Bayesian Model Assessment for Jointly Modeling Multidimensional Response Data With Application to Computerized Testing

• In Event: AERA Virtual Poster Session 7
  In Poster Session: Poster Session

Abstract

We propose a joint model including an item response theory model for the response data, a log-normal model for the continuous RT data, and a normal model for a pencil-and-paper score. Then, we reformulate and reparameterize the model to capture the relationship between the model parameters to facilitate the prior specification and Bayesian computation. Further, we develop several new model assessment criteria based on the decomposition of deviance information criterion and the logarithm of the pseudo-marginal likelihood. The proposed criteria can quantify the improvement in the fit of one part of the multidimensional data given the other parts. Finally, the simulation shows the proposed model assessment criteria work well, and we illustrate the use of them via a computerized testing.

Authors

• Fang Liu

• Xiaojing Wang, University of Connecticut

• Roeland Hancock

• Ming-Hui Chen