Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
StateAccepted/In press - 2023


  • Bayesian
  • cognitive diagnosis model
  • identifiability
  • nominal response data
  • restricted latent class models

ASJC Scopus subject areas

  • Applied Mathematics
  • General Psychology


Dive into the research topics of 'Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation'. Together they form a unique fingerprint.

Cite this