IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v25y2000i2p225-247.html
   My bibliography  Save this article

Using Loss Functions for DIF Detection: An Empirical Bayes Approach

Author

Listed:
  • Rebecca Zwick
  • Dorothy T. Thayer
  • Charles Lewis

Abstract

We investigated a DIF flagging method based on loss functions. The approach builds on earlier research that involved the development of an empirical Bayes (EB) enhancement to Mantel-Haenszel (MH) DIF analysis. The posterior distribution of DIF parameters was estimated and used to obtain the posterior expected loss for the proposed approach and for competing classification rules. Under reasonable assumptions about the relative seriousness of Type I and Type II errors, the loss-function-based DIF detection rule was found to perform better than the commonly used "A, " "B, " and "C" DIF classification system, especially in small samples.

Suggested Citation

  • Rebecca Zwick & Dorothy T. Thayer & Charles Lewis, 2000. "Using Loss Functions for DIF Detection: An Empirical Bayes Approach," Journal of Educational and Behavioral Statistics, , vol. 25(2), pages 225-247, June.
    • Handle: RePEc:sae:jedbes:v:25:y:2000:i:2:p:225-247
    DOI: 10.3102/10769986025002225
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986025002225
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986025002225?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

    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:25:y:2000:i:2:p:225-247. 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.