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Using Ordering Theory to Learn Attribute Hierarchies From Examinees’ Attribute Profiles

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  • Yuzhi Yan
  • Shenghong Dong
  • Xiaofeng Yu

Abstract

In cognitive diagnosis, attribute hierarchies are considered important structural features of cognitive diagnostic models, as they provide auxiliary information about the nature of attributes. In this article, the idea of ordering theory is applied to cognitive diagnosis, and a new approach to identify attribute hierarchy based on the attribute correlation intensity matrix is proposed. This approach attempts to identify attribute hierarchy in data with a small sample size while ensuring a high accuracy rate. The results of simulation studies and empirical data analysis show that the proposed approach can be used to identify attribute hierarchy in diagnostic tests, especially in small samples, making it worth popularizing.

Suggested Citation

  • Yuzhi Yan & Shenghong Dong & Xiaofeng Yu, 2025. "Using Ordering Theory to Learn Attribute Hierarchies From Examinees’ Attribute Profiles," Journal of Educational and Behavioral Statistics, , vol. 50(6), pages 985-1013, December.
    • Handle: RePEc:sae:jedbes:v:50:y:2025:i:6:p:985-1013
    DOI: 10.3102/10769986241280389
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    References listed on IDEAS

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    4. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2017. "Regularized latent class analysis with application in cognitive diagnosis," LSE Research Online Documents on Economics 103182, London School of Economics and Political Science, LSE Library.
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