A multiple logistic regression-based (MLR-B) Q-matrix validation method for cognitive diagnosis models:A confirmatory approach

Dongbo Tu, Jin Chiu, Wenchao Ma, Daxun Wang, Yan Cai, Xueyuan Ouyang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Q-matrix is an essential component specifying the relationship between attributes and items, which plays a key role in cognitive diagnosis assessment. The Q-matrix is usually developed by domain experts and its specifications tend to be subjective and might have misspecifications. Many existing pieces of research concentrate on the validation of Q-matrix; however, few of them can be applied to saturated cognitive diagnosis models. This paper proposes a general and effective Q-matrix validation method by employing multiple logistic regression model. Simulation studies are carried out to investigate the performance of the proposed method and compare it with four existing methods. Simulation results indicate the proposed method outperforms the existing methods in terms of validation accuracy. In addition, a set of real data is used as an example to illustrate its application. Finally, we discuss the limitations of the current study and the directions of future studies.

Original languageEnglish (US)
Pages (from-to)2080-2092
Number of pages13
JournalBehavior Research Methods
Volume55
Issue number4
DOIs
StatePublished - Jun 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022, The Psychonomic Society, Inc.

Keywords

  • GDM
  • LCDM
  • multiple logistic regression
  • Q-matrix misspecifications
  • validation

PubMed: MeSH publication types

  • Journal Article

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