Statistical Analysis of Q-Matrix Based Diagnostic Classification Models

Yunxiao Chen, Jingchen Liu, Gongjun Xu, Zhiliang Ying

Research output: Contribution to journalArticle

55 Scopus citations

Abstract

Diagnostic classification models (DMCs) have recently gained prominence in educational assessment, psychiatric evaluation, and many other disciplines. Central to the model specification is the so-called Q-matrix that provides a qualitative specification of the item-attribute relationship. In this article, we develop theories on the identifiability for the Q-matrix under the DINA and the DINO models. We further propose an estimation procedure for the Q-matrix through the regularized maximum likelihood. The applicability of this procedure is not limited to the DINA or the DINO model and it can be applied to essentially all Q-matrix based DMCs. Simulation studies show that the proposed method admits high probability recovering the true Q-matrix. Furthermore, two case studies are presented. The first case is a dataset on fraction subtraction (educational application) and the second case is a subsample of the National Epidemiological Survey on Alcohol and Related Conditions concerning the social anxiety disorder (psychiatric application).

Original languageEnglish (US)
Pages (from-to)850-866
Number of pages17
JournalJournal of the American Statistical Association
Volume110
Issue number510
DOIs
StatePublished - Apr 3 2015

Keywords

  • Diagnostic classification models
  • Identifiability
  • Latent variable selection

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