The influence of sample size, effect size, and percentage of DIF items on the performance of the Mantel-Haenszel and logistic regression DIF identification procedures.

The frequent use of standardized tests for admission, advancement, and accreditation has increased public awareness of measurement issues, in particular, test and item bias. The logistic regression (LR) and Mantel-Haenszel (MH) procedures are relatively new methods of detecting item bias or differential item functioning (DIF) in tests. In only a few studies has the performance of these two procedures been compared. In the present study, sample size, effect size, and percentage of DIF items in the test were manipulated in order to compare detection rates of uniform DIF by the LR and MH procedures. Simulated data, with known amounts of DIF, were used to evaluate the effects of these variables on DIF detection rates. In detecting uniform DIF, the LR procedure had a slight advantage over the MH procedure at the cost of increased false positive rates. P-value difference was definitely a more accurate measure of the amount of DIF than b value difference. (Abstract shortened by UMI.