Estimation of Generalized DINA Model with Order Restrictions

Chen Yu Hong, Yu Wei Chang, Rung Ching Tsai*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Cognitive diagnostic models provide valuable information on whether a student has mastered each of the attributes a test intends to evaluate. Despite its generality, the generalized DINA model allows for the possibility of lower correct rates for students who master more attributes than those who know less. This paper considers the use of order-constrained parameter space of the G-DINA model to avoid such a counter-intuitive phenomenon and proposes two algorithms, the upward and downward methods, for parameter estimation. Through simulation studies, we compare the accuracy in parameter estimation and in classification of attribute patterns obtained from the proposed two algorithms and the current approach when the restricted parameter space is true. Our results show that the upward method performs the best among the three, and therefore it is recommended for estimation, regardless of the distribution of respondents’ attribute patterns, types of test items, and the sample size of the data.

Original languageEnglish
Pages (from-to)460-484
Number of pages25
JournalJournal of Classification
Issue number3
Publication statusPublished - 2016 Oct 1


  • Classification of attribute patterns
  • Cognitive diagnostic model
  • G-DINA model
  • Order restrictions

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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