Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models

Yinghan Chen and Shiyu Wang
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Yinghan Chen: University of Nevada Reno
Shiyu Wang: University of Georgia

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 6, 810-841

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.

Keywords: cognitive diagnosis models; directed acyclic graphs; hierarchical structure (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:6:p:810-841

DOI: 10.3102/10769986231174918

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