Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models

Autor/inn/en	Chen, Yinghan; Wang, Shiyu
Titel	Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 6, S.810-841 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chen, Yinghan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986231174918
Schlagwörter	Cognitive Measurement; Models; Bayesian Statistics; Computation; Algorithms; Language Tests + Suchen Sie Ihr Suchwort? Analogiemodell; Algorithm; Algorithmus; Language test; Sprachtest
Abstract	Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a Bayesian formulation for attribute hierarchy within CDM framework and developed an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy along with the specified CDM parameters. Our proposed estimation method is flexible and can be adapted to a general class of CDMs. We demonstrated our proposed method via a simulation study, and the results from which show that the proposed method can fully recover or estimate at least a subgraph of the underlying structure across various conditions under a specified CDM model. The real data application indicates the potential of learning attribute structure from data using our algorithm and validating the existing attribute hierarchy specified by content experts. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01