ArticlePsychometrika2023
Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models.
Article in Psychometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Uncovering Latent Structures: A Bayesian Approach to Estimating Q-Matrix and Attribute Hierarchies in Cognitive Diagnostic Models.Psychometrika · 2026Article
- A Novel Method for Detecting Intersectional DIF: Multilevel Random Item Effects Model with Regularized Gaussian Variational Estimation.Psychometrika · 2025Article
- Detecting Differential Item Functioning across Multiple Groups using Group Pairwise Penalty.Psychometrika · 2025Article
- Consistency Theory of General Nonparametric Classification Methods in Cognitive Diagnosis.Psychometrika · 2025Article
- Validating attribute hierarchies in cognitive diagnosis models.Frontiers in psychology · 2025Article
- AI-enhanced adaptive testing with cognitive diagnostic feedback and its association with performance in undergraduate surgical education: a pilot study.Frontiers in behavioral neuroscience · 2025Article
- New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.Psychometrika · 2024Article
- An exploratory Q-matrix estimation method based on sparse non-negative matrix factorization.Behavior research methods · 2024Article
- Novel item selection strategies for cognitive diagnostic computerized adaptive testing: A heuristic search framework.Behavior research methods · 2024Article
- A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses.Psychometrika · 2023Article
- A Testlet Diagnostic Classification Model with Attribute Hierarchies.Applied psychological measurement · 2023Article
- A novel brain functional-structural hybrid analysis to explain the effect of a 6-month psychosocial intervention on resilience in breast cancer.International journal of clinical and health psychology : IJCHPArticle
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3 authors.
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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.
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