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Meta-Analysis Of Type I Error Rates For Detecting Differential Item Functioning With Logistic Regression And Mantel-Haenszel In Monte Carlo Studies, Eva Van De Water Ph. D.
Meta-Analysis Of Type I Error Rates For Detecting Differential Item Functioning With Logistic Regression And Mantel-Haenszel In Monte Carlo Studies, Eva Van De Water Ph. D.
Eva Van De Water
Differential item functioning (DIF) occurs when individuals from different groups who have equal levels of a latent trait fail to earn commensurate scores on a testing instrument. Type I error occurs when DIF-detection methods result in unbiased items being excluded from the test while a Type II error occurs when biased items remain on the test after DIF-detection methods have been employed. Both errors create potential issues of injustice amongst examinees and can result in costly and protracted legal action. The purpose of this research was to evaluate two methods for detecting DIF: logistic regression (LR) and Mantel-Haenszel (MH).
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