| DIF detection methods identify the difference between the performances of subgroups when the subgroups are matched by examinees' ability level or a proxy variable, such as total test score (Holland & Wainer, 1993). Log-linear Models (LLM) method is one of the DIF detection methods. This method was first introduced by Mellenbergh (1982) to investigate the relationship among item responses, subgroups, and categorized total test score in terms of DIF detection.;This study examined the performance of LLM as a DIF detection method for dichotomous items and polytomous items. LLM method was compared with Mantel-Haenszsel (MH) and logistic regression (LR) methods to detect uniform DIF and with LR to detect non-uniform DIF in dichotomous item response data. MH was not included in non-uniform DIF detection, because, the previous studies indicated that it is not able to detect non-uniform DIF (Narayanon & Swaminathan, 1996; Uttaro & Milsap, 1994). In addition, LLM was compared with Mantel, generalized Mantel-Haenszsel (GMH), ordinal logistic regression (OLR), logistic discriminate function analysis (LDFA) methods in polytomous item response data. For this purpose, both simulation study and empirical study were conducted under various sample sizes, ability mean differences (impact) and item parameters. Since the previous studies did not investigate the effect of ability mean differences on DIF detection with LLM, this study also focused on the effect of ability mean differences between subgroups. This study found that MH was better to detect uniform DIF when LR and LLM indicated equally well performance on uniform and non-uniform DIF detection. In Addition, GMH and LLM performed better than Mantel, OLR, and LDFA for the polytomous item response data. |
