Identifiability of restricted latent class models with binary responses

Gongjun Xu

111 citations0/0 files storedThe Annals of Statistics

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models

Abstract

Statistical latent class models are widely used in social and psychological researches, yet it is often difficult to establish the identifiability of the model parameters. In this paper, we consider the identifiability issue of a family of restricted latent class models, where the restriction structures are needed to reflect pre-specified assumptions on the related assessment. We establish the identifiability results in the strict sense and specify which types of restriction structure would give the identifiability of the model parameters. The results not only guarantee the validity of many of the popularly used models, but also provide a guideline for the related experimental design, where in the current applications the design is usually experience based and identifiability is not guaranteed. Theoretically, we develop a new technique to establish the identifiability result, which may be extended to other restricted latent class models.

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References

1974Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models 2001Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory 1983RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY 2011The Generalized DINA Model Framework 2010Diagnostic measurement: Theory, methods, and applications.2006Measurement of psychological disorders using cognitive diagnosis models.2004Higher-Order Latent Trait Models for Cognitive Diagnosis 2008Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

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