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Using Loss Functions for DIF Detection: An Empirical Bayes Approach

Rebecca Zwick, Dorothy T. Thayer and Charles Lewis
Journal of Educational and Behavioral Statistics
Vol. 25, No. 2 (Summer, 2000), pp. 225-247
Stable URL: http://www.jstor.org/stable/1165333
Page Count: 23
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Using Loss Functions for DIF Detection: An Empirical Bayes Approach
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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.

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