Journal article
Model Selection Posterior Predictive Model Checking via Limited‐Information Indices for Bayesian Diagnostic Classification Modeling
Journal of educational measurement, Vol.61(4), pp.740-762
12/2024
DOI: 10.1111/jedm.12408
Appears in UI Libraries Support Open Access
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.
Details
- Title: Subtitle
- Model Selection Posterior Predictive Model Checking via Limited‐Information Indices for Bayesian Diagnostic Classification Modeling
- Creators
- Jihong Zhang - University of Arkansas at FayettevilleJonathan Templin - University of IowaXinya Liang - University of Arkansas at Fayetteville
- Resource Type
- Journal article
- Publication Details
- Journal of educational measurement, Vol.61(4), pp.740-762
- DOI
- 10.1111/jedm.12408
- ISSN
- 0022-0655
- eISSN
- 1745-3984
- Publisher
- Wiley
- Language
- English
- Electronic publication date
- 07/15/2024
- Date published
- 12/2024
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984658259102771
Metrics
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