ArticlePsychometrika2024
Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation.
Article in Psychometrika, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 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
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Restricted latent class models (RLCMs) provide an important framework for diagnosing and classifying respondents on a collection of multivariate binary responses. Recent research made significant advances in theory for establishing identifiability conditions for RLCMs with binary and polytomous response data. Multiclass data, which are unordered nominal response data, are also widely collected in the social sciences and psychometrics via forced-choice inventories and multiple choice tests. We establish new identifiability conditions for parameters of RLCMs for multiclass data and discuss the implications for substantive applications. The new identifiability conditions are applicable to a wealth of RLCMs for polytomous and nominal response data. We propose a Bayesian framework for inferring model parameters, assess parameter recovery in a Monte Carlo simulation study, and present an application of the model to a real dataset.
Indexed as
Identifiers
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Registered trials
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