A Class of Cognitive Diagnosis Models for Polytomous Data

Autor/inn/en	Gao, Xuliang; Ma, Wenchao; Wang, Daxun; Cai, Yan; Tu, Dongbo
Titel	A Class of Cognitive Diagnosis Models for Polytomous Data
Quelle	In: Journal of Educational and Behavioral Statistics, 46 (2021) 3, S.297-322 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ma, Wenchao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998620951986
Schlagwörter	Cognitive Measurement; Models; Test Items; Scoring; Computation; Cognitive Tests; Grade 8; Mathematics Tests; Foreign Countries; China + Suchen Sie Ihr Suchwort? Analogiemodell; Test content; Testaufgabe; Bewertung; Kognitiver Fähigkeitstest; School year 08; 8. Schuljahr; Schuljahr 08; Ausland
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01