A Note on Weaker Conditions for Identifying Restricted Latent Class Models for Binary Responses

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Restricted latent class models (RLCMs) are an important class of methods that provide researchers and practitioners in the educational, psychological, and behavioral sciences with fine-grained diagnostic information to guide interventions. Recent research established sufficient conditions for identifying RLCM parameters. A current challenge that limits widespread application of RLCMs is that existing identifiability conditions may be too restrictive for some practical settings. In this paper we establish a weaker condition for identifying RLCM parameters for multivariate binary data. Although the new results weaken identifiability conditions for general RLCMs, the new results do not relax existing necessary and sufficient conditions for the simpler DINA/DINO models. Theoretically, we introduce a new form of latent structure completeness, referred to as dyad-completeness, and prove identification by applying Kruskal’s Theorem for the uniqueness of three-way arrays. The new condition is more likely satisfied in applied research, and the results provide researchers and test-developers with guidance for designing diagnostic instruments.

Original languageEnglish (US)
Pages (from-to)158-174
Number of pages17
Issue number1
StatePublished - Mar 2023


  • Q-matrix
  • cognitive diagnosis
  • identifiability
  • restricted latent class models

ASJC Scopus subject areas

  • Applied Mathematics
  • General Psychology


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