ArticlePsychometrika2021
Inferring the Number of Attributes for the Exploratory DINA Model.
Article in Psychometrika, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A general diagnostic modelling framework for forced-choice assessments.The British journal of mathematical and statistical psychology · 2025Article
- Explaining Performance Gaps with Problem-Solving Process Data via Latent Class Mediation Analysis.Psychometrika · 2025Article
- An exploratory Q-matrix estimation method based on sparse non-negative matrix factorization.Behavior research methods · 2024Article
- Cognitive diagnostic assessment: A Q-matrix constraint-based neural network method.Behavior research methods · 2024Article
- Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation.Psychometrika · 2024Article
- Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs).Psychometrika · 2024Article
- Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models.Psychometrika · 2023Article
- Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis.Psychometrika · 2023Article
- A Note on Weaker Conditions for Identifying Restricted Latent Class Models for Binary Responses.Psychometrika · 2023Article
- Generic Identifiability of the DINA Model and Blessing of Latent Dependence.Psychometrika · 2023Article
- Development of an assessment technique for basic science retention using the NBME subject exam data.BMC medical education · 2022Article
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4 authors.
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Abstract
Diagnostic classification models (DCMs) are widely used for providing fine-grained classification of a multidimensional collection of discrete attributes. The application of DCMs requires the specification of the latent structure in what is known as the [Formula: see text] matrix. Expert-specified [Formula: see text] matrices might be biased and result in incorrect diagnostic classifications, so a critical issue is developing methods to estimate [Formula: see text] in order to infer the relationship between latent attributes and items. Existing exploratory methods for estimating [Formula: see text] must pre-specify the number of attributes, K. We present a Bayesian framework to jointly infer the number of attributes K and the elements of [Formula: see text]. We propose the crimp sampling algorithm to transit between different dimensions of K and estimate the underlying [Formula: see text] and model parameters while enforcing model identifiability constraints. We also adapt the Indian buffet process and reversible-jump Markov chain Monte Carlo methods to estimate [Formula: see text]. We report evidence that the crimp sampler performs the best among the three methods. We apply the developed methodology to two data sets and discuss the implications of the findings for future research.
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