Document Type
Article
Publication Date
12-2024
Keywords
GOODNESS-OF-FIT; RESPONSE THEORY MODELS; CROSS-VALIDATION; FAMILY; MATRIX; TESTS
Abstract
Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model evaluation tool, which allows researchers to detect Q-matrix misspecification. However, model selection methods using posterior predictive checking (PPC) for Bayesian DCM are not well investigated. Thus, this research aims to propose a novel model selection approach using posterior predictive checking with limited-information statistics for selecting the correct Q-matrix. A simulation study was conducted to examine the performance of the proposed method. Furthermore, an empirical example was provided to illustrate how it can be used in real scenarios.
Citation
Zhang, J., Templin, J., & Liang, X. (2024). Model Selection Posterior Predictive Model Checking via Limited-Information Indices for Bayesian Diagnostic Classification Modeling. Journal of Educational Measurement, 61 (4), 740-762. https://doi.org/10.1111/jedm.12408
Comments
This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.