Exploring Differential Item Functioning in PISA 2015 Science Test with the Rasch-tree

Abstract

The purpose of this study is to introduce the Rasch-tree, which is Rasch model-based recursive partitioning, as a new method for differential item functioning(DIF) detection and to examine its usefulness by exploring DIF in PISA 2015 Science test data with the Rasch-tree. This study used 16 items binary response of PISA 2015 Science test and 41 variables related to the student, parent, family, teacher, science, and ICT. The results of this study showed that there are four subgroups splitted by the three variables, ‘Enjoyment of Science(JOYSCICE)’, ‘gender’, and ‘Child’s past science activities(PRESUPP)’, and the two DIF items were detected. These items had large differences in the estimate item difficulty parameters between these four subgroups, especially the group of students with high level of JOYSCIE and the group of female students with relatively low level of JOYSCIE. For these results, it was confirmed that the Rasch-tree could be effective for exploring DIF between latent classes by considering simultaneously multiple trait variables.