Examining and Improving the Gender and Language DIF in the VERA 8 Tests

Abstract

The purpose of this study was to examine and improve differential item functioning (DIF) across gender and language groups in the VERA 8 tests. We used multigroup concurrent calibration with full and partial invariance based on the Rasch and two-parameter logistic (2PL) models, and classified students into proficiency levels based on their test scores and previously defined cut scores. The results indicated that some items showed gender- and language-specific DIF when using the Rasch model, but we did not detect large misfit items (suspected as DIF) when using the 2PL model. When the item parameters were estimated using the 2PL model with partial invariance assumption (PI-2PL), only small or negligible misfit items were found in the overall tests for both groups. It is argued in this study that the 2PL model should be preferred because both of its approaches provided less bias. However, especially in the presence of unweighted sample sizes of German and non-German students, the non-German students had the highest misfit item proportions. Although the items with medium or small misfit did not have a significant effect on the scores and performance classifications, the items with large misfit changed the proportions of students at the highest and lowest performance levels.