ArticlePsychometrika2019
An Exploratory Diagnostic Model for Ordinal Responses with Binary Attributes: Identifiability and Estimation.
Article in Psychometrika, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- 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
- 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
- Identifiability of Latent Class Models with Covariates.Psychometrika · 2022Article
- Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm.Psychometrika · 2022Article
- Measuring students' learning progressions in energy using cognitive diagnostic models.Frontiers in psychology · 2022Article
- Inferring the Number of Attributes for the Exploratory DINA Model.Psychometrika · 2021Article
- A Constrained Metropolis-Hastings Robbins-Monro Algorithm for Q Matrix Estimation in DINA Models.Psychometrika · 2020Article
- Measuring Skill Growth and Evaluating Change: Unconditional and Conditional Approaches to Latent Growth Cognitive Diagnostic Models.Frontiers in psychology · 2020Article
- On the Sequential Hierarchical Cognitive Diagnostic Model.Frontiers in psychology · 2020Article
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1 author.
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Abstract
Diagnostic models (DMs) provide researchers and practitioners with tools to classify respondents into substantively relevant classes. DMs are widely applied to binary response data; however, binary response models are not applicable to the wealth of ordinal data collected by educational, psychological, and behavioral researchers. Prior research developed confirmatory ordinal DMs that require expert knowledge to specify the underlying structure. This paper introduces an exploratory DM for ordinal data. In particular, we present an exploratory ordinal DM, which uses a cumulative probit link along with Bayesian variable selection techniques to uncover the latent structure. Furthermore, we discuss new identifiability conditions for structured multinomial mixture models with binary attributes. We provide evidence of accurate parameter recovery in a Monte Carlo simulation study across moderate to large sample sizes. We apply the model to twelve items from the public-use, Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 approaches to learning and self-description questionnaire and report evidence to support a three-attribute solution with eight classes to describe the latent structure underlying the teacher and parent ratings. In short, the developed methodology contributes to the development of ordinal DMs and broadens their applicability to address theoretical and substantive issues more generally across the social sciences.
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