A Class of Cognitive Diagnosis Models for Polytomous Data. (June 2021)

• Record Type: Journal Article
• Authors: Gao, Xuliang; Ma, Wenchao; Wang, Daxun; Cai, Yan; Tu, Dongbo
• Is Part Of: Journal of educational and behavioral statistics. Volume 46:Number 3(2021)
• Page Start: 297
• Page End: 322
• Publication Date: 2021-06
• Subjects: cognitive diagnosis -- polytomously scored items -- polytomous CDMs; Educational statistics -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 370.2
• DOI: 10.3102/1076998620951986
• Languages: English
• ISSNs: 1076-9986

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.