IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v50y2025i6p985-1013.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986241280389
    Download Restriction: no

    File URL: https://libkey.io/10.3102/10769986241280389?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:50:y:2025:i:6:p:985-1013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.