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Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models

Published online by Cambridge University Press:  01 January 2025

Chenchen Ma ,
Jing Ouyang  and
Gongjun Xu Open the ORCID record for Gongjun Xu [Opens in a new window]
Chenchen Ma
Affiliation:
University of Michigan
Jing Ouyang
Affiliation:
University of Michigan
Gongjun Xu*
Affiliation:
University of Michigan
*
Correspondence should be made to Gongjun Xu, Department of Statistics, University of Michigan, 456 West Hall, 1085 South University, Ann Arbor, MI48109, USA. Email: [email protected]
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Abstract

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in educational and psychological measurement. A key component of CDMs is the Q-matrix characterizing the dependence structure between the items and the latent attributes. Additionally, researchers also assume in many applications certain hierarchical structures among the latent attributes to characterize their dependence. In most CDM applications, the attribute–attribute hierarchical structures, the item-attribute Q-matrix, the item-level diagnostic models, as well as the number of latent attributes, need to be fully or partially pre-specified, which however may be subjective and misspecified as noted by many recent studies. This paper considers the problem of jointly learning these latent and hierarchical structures in CDMs from observed data with minimal model assumptions. Specifically, a penalized likelihood approach is proposed to select the number of attributes and estimate the latent and hierarchical structures simultaneously. An expectation-maximization (EM) algorithm is developed for efficient computation, and statistical consistency theory is also established under mild conditions. The good performance of the proposed method is illustrated by simulation studies and real data applications in educational assessment.

Keywords

cognitive diagnosis modelhierarchical structureQ-matrixpenalized EM algorithm
Type
Theory and Methods
Information
Psychometrika , Volume 88 , Issue 1 , March 2023 , pp. 175 - 207
Copyright
Copyright © 2022 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/S0033312300006074.

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