A Class of Cognitive Diagnosis Models for Polytomous Data

Xuliang Gao, Wenchao Ma, Daxun Wang, Yan Cai and Dongbo Tu
Additional contact information
Xuliang Gao: 12642Jiangxi Normal University
Wenchao Ma: The 8063University of Alabama
Dongbo Tu: 12642Jiangxi Normal University

Journal of Educational and Behavioral Statistics, 2021, vol. 46, issue 3, 297-322

Abstract: This article proposes a class of cognitive diagnosis models (CDMs) for polytomously scored items with different link functions. Many existing polytomous CDMs can be considered as special cases of the proposed class of polytomous CDMs. Simulation studies were carried out to investigate the feasibility of the proposed CDMs and the performance of several information criteria (Akaike’s information criterion [AIC], consistent Akaike’s information criterion [CAIC], and Bayesian information criterion [BIC]) in model selection. The results showed that the parameters of the proposed CDMs could be recovered adequately under varied conditions. In addition, CAIC and BIC had better performance in selecting the most appropriate model than AIC. Finally, a set of real data was analyzed to illustrate the application of the proposed CDMs.

Keywords: cognitive diagnosis; polytomously scored items; polytomous CDMs (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:46:y:2021:i:3:p:297-322

DOI: 10.3102/1076998620951986

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