Literature DB >> 35254608

Identifiability of Latent Class Models with Covariates.

Jing Ouyang1, Gongjun Xu2.   

Abstract

Latent class models with covariates are widely used for psychological, social, and educational research. Yet the fundamental identifiability issue of these models has not been fully addressed. Among the previous research on the identifiability of latent class models with covariates, Huang and Bandeen-Roche (Psychometrika 69:5-32, 2004) studied the local identifiability conditions. However, motivated by recent advances in the identifiability of the restricted latent class models, particularly cognitive diagnosis models (CDMs), we show in this work that the conditions in Huang and Bandeen-Roche (Psychometrika 69:5-32, 2004) are only necessary but not sufficient to determine the local identifiability of the model parameters. To address the open identifiability issue for latent class models with covariates, this work establishes conditions to ensure the global identifiability of the model parameters in both strict and generic sense. Moreover, our results extend to the polytomous-response CDMs with covariates, which generalizes the existing identifiability results for CDMs.
© 2022. The Author(s) under exclusive licence to The Psychometric Society.

Entities:  

Keywords:  cognitive diagnosis models; identifiability; latent class models

Year:  2022        PMID: 35254608     DOI: 10.1007/s11336-022-09852-y

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  1 in total

1.  Nested partially latent class models for dependent binary data; estimating disease etiology.

Authors:  Zhenke Wu; Maria Deloria-Knoll; Scott L Zeger
Journal:  Biostatistics       Date:  2017-04-01       Impact factor: 5.899

  1 in total

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