ArticleApplied psychological measurement2021
Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints.
Article in Applied psychological measurement, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From theory to practice: A comprehensive toolkit for Q-matrix validation in cognitive diagnosis.Behavior research methods · 2026Pooled it
- A general diagnostic modelling framework for forced-choice assessments.The British journal of mathematical and statistical psychology · 2025Article
- A cognitive diagnosis model for disengaged behaviors.Behavior research methods · 2025Article
- Introducing a blocked procedure in nonparametric CD-CAT.PloS one · 2024Article
- Supervised diagnostic classification of cognitive attributes using data augmentation.PloS one · 2024Article
- Improving reliability estimation in cognitive diagnosis modeling.Behavior research methods · 2023Article
- Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm.Psychometrika · 2022Article
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2 authors.
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Abstract
Despite the increasing popularity, cognitive diagnosis models have been criticized for limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) estimation and monotonic constraints to stabilize item parameter estimation and facilitate person classification in small samples based on the generalized deterministic input noisy "and" gate (G-DINA) model. Both simulation study and real data analysis were used to assess the utility of the BM estimation and monotonic constraints. Results showed that in small samples, (a) the G-DINA model with BM estimation is more likely to converge successfully, (b) when prior distributions are specified reasonably, and monotonicity is not violated, the BM estimation with monotonicity tends to produce more stable item parameter estimates and more accurate person classification, and (c) the G-DINA model using the BM estimation with monotonicity is less likely to overfit the data and shows higher predictive power.
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