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

Published online by Cambridge University Press:  01 January 2025

Fang Liu ,
Xiaojing Wang Open the ORCID record for Xiaojing Wang [Opens in a new window] ,
Roeland Hancock  and
Ming-Hui Chen
Fang Liu
Affiliation:
Northeast Normal University
Xiaojing Wang*
Affiliation:
University of Connecticut
Roeland Hancock
Affiliation:
University of Connecticut
Ming-Hui Chen
Affiliation:
University of Connecticut
*
Correspondence should be made to Xiaojing Wang, University of Connecticut, Storrs, CT 06250, USA. Email: xiaojing.wang@uconn.edu; URL: https://xiaojing-wang.uconn.edu
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Abstract

Computerized assessment provides rich multidimensional data including trial-by-trial accuracy and response time (RT) measures. A key question in modeling this type of data is how to incorporate RT data, for example, in aid of ability estimation in item response theory (IRT) models. To address this, we propose a joint model consisting of a two-parameter IRT model for the dichotomous item response data, a log-normal model for the continuous RT data, and a normal model for corresponding paper-and-pencil scores. Then, we reformulate and reparameterize the model to capture the relationship between the model parameters, to facilitate the prior specification, and to make the Bayesian computation more efficient. Further, we propose several new model assessment criteria based on the decomposition of deviance information criterion (DIC) the logarithm of the pseudo-marginal likelihood (LPML). The proposed criteria can quantify the improvement in the fit of one part of the multidimensional data given the other parts. Finally, we have conducted several simulation studies to examine the empirical performance of the proposed model assessment criteria and have illustrated the application of these criteria using a real dataset from a computerized educational assessment program.

Keywords

computerized testsDIC decompositionIRT modelsLPML decompositionpaper-and-pencil testsresponse times

Information

Type
Theory and Methods
Information
Psychometrika , Volume 87 , Issue 4 , December 2022 , pp. 1290 - 1317
Copyright
Copyright © 2022 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/S0033312300005470a.

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