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Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models
Na Shan; Ping-Feng Xu – Journal of Educational and Behavioral Statistics, 2025
The detection of differential item functioning (DIF) is important in psychological and behavioral sciences. Standard DIF detection methods perform an item-by-item test iteratively, often assuming that all items except the one under investigation are DIF-free. This article proposes a Bayesian adaptive Lasso method to detect DIF in graded response…
Descriptors: Bayesian Statistics, Item Response Theory, Adolescents, Longitudinal Studies
Sinharay, Sandip; Dorans, Neil J.; Grant, Mary C.; Blew, Edwin O. – Journal of Educational and Behavioral Statistics, 2009
Test administrators often face the challenge of detecting differential item functioning (DIF) with samples of size smaller than that recommended by experts. A Bayesian approach can incorporate, in the form of a prior distribution, existing information on the inference problem at hand, which yields more stable estimation, especially for small…
Descriptors: Test Bias, Computation, Bayesian Statistics, Data
Wang, Xiaohui; Bradlow, Eric T.; Wainer, Howard; Muller, Eric S. – Journal of Educational and Behavioral Statistics, 2008
In the course of screening a form of a medical licensing exam for items that function differentially (DIF) between men and women, the authors used the traditional Mantel-Haenszel (MH) statistic for initial screening and a Bayesian method for deeper analysis. For very easy items, the MH statistic unexpectedly often found DIF where there was none.…
Descriptors: Bayesian Statistics, Licensing Examinations (Professions), Medicine, Test Items