Akademik Veri Yönetim Sistemi
Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, cilt.14, sa.3, ss.200-221, 2023 (ESCI)
This study aimed to compare differential item functioning (DIF) and differential step function (DSF) detection methods in polytomously scored items under various conditions. In this context, the study examined Kazakhstan, Turkey and USA data obtained from the items related to the frequency of using digital devices at school in PISA 2018 students’ “ICT Familiarity Questionnaire”. Mantel test, Liu-Agresti statistics, Cox β and poly-SIBTEST methods were used for polytomous DIF analysis while Adjacent Category Logistic Regression Model and Cumulative Category Log Odds Ratio methods were used for DSF analysis. This study was carried out with correlational survey model, by using “differential category combining, focus group sample size, focus group: reference group sample ratio and DIF/DSF detection method”. SAS and R software were utilized in the creation of conditions; SIBTEST was used for poly-SIBTEST for analysis and DIFAS programs were used for the other methods. Analyses demonstrated that the number of items/steps exhibiting high level of DIF/DSF was higher in the small sample according to polytomous DIF methods and in the large sample compared to DSF methods. During the steps, it was stated that the DIF value was lower in the items containing DSF with the opposite sign; therefore, not performing DSF analysis in an item with no DIF may yield erroneous results. Although the differential category combining conditions created within the scope of the research did not have a systematic effect on the results, it was suggested to examine this situation in future studies, considering that the frequency of marking the combined categories differentiated the results.