ArticlePsychometrika2020
A Sparse Latent Class Model for Cognitive Diagnosis.
Article in Psychometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- A Review of R Packages for Bayesian Model-based Clustering of High-dimensional Multivariate Environmental Exposures.Current environmental health reports · 2026Review
- ConstructivePsychometrika · 2026Article
- A sparse latent class model incorporating response times.The British journal of mathematical and statistical psychology · 2025Article
- Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models.Psychometrika · 2025Article
- Exploratory General-Response Cognitive Diagnostic Models with Higher-Order Structures.Psychometrika · 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
- Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation.Psychometrika · 2024Article
- Sufficient and Necessary Conditions for the Identifiability of DINA Models with Polytomous Responses.Psychometrika · 2024Article
- Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs).Psychometrika · 2024Article
- A multiple logistic regression-based (MLR-B) Q-matrix validation method for cognitive diagnosis models:A confirmatory approach.Behavior research methods · 2023Article
- Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models.Psychometrika · 2023Article
- Scalable Bayesian Approach for the Dina Q-Matrix Estimation Combining Stochastic Optimization and Variational Inference.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
- A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis.Journal of the American Statistical Association · 2023Article
- Advancing a cross-cultural understanding of teacher perceptions of school climate: A latent class analysis using 2018 TALIS data.Frontiers in psychology · 2023Article
- Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm.Psychometrika · 2022Article
- Identifiability of Latent Class Models with Covariates.Psychometrika · 2022Article
- Efficient Metropolis-Hastings Robbins-Monro Algorithm for High-Dimensional Diagnostic Classification Models.Applied psychological measurement · 2022Article
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3 authors.
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Abstract
Cognitive diagnostic models (CDMs) are latent variable models developed to infer latent skills, knowledge, or personalities that underlie responses to educational, psychological, and social science tests and measures. Recent research focused on theory and methods for using sparse latent class models (SLCMs) in an exploratory fashion to infer the latent processes and structure underlying responses. We report new theoretical results about sufficient conditions for generic identifiability of SLCM parameters. An important contribution for practice is that our new generic identifiability conditions are more likely to be satisfied in empirical applications than existing conditions that ensure strict identifiability. Learning the underlying latent structure can be formulated as a variable selection problem. We develop a new Bayesian variable selection algorithm that explicitly enforces generic identifiability conditions and monotonicity of item response functions to ensure valid posterior inference. We present Monte Carlo simulation results to support accurate inferences and discuss the implications of our findings for future SLCM research and educational testing.
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