differential-item-functioning
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- PaperLanguage Testing10 Jun 2026
Beyond Traditional Differential Item Functioning Detection: A Rasch Tree Approach to Evaluating Item Fairness in a Large-Scale German Reading Comprehension Test
Farshad Effatpanah, Olga Kunina-Habenicht, Katharina Antonia Michiko Tremmel, Philipp Sonnleitner
This study applies a recursive partitioning Rasch tree model to detect differential item functioning (DIF) in a large-scale German reading comprehension test for fifth graders, examining effects of gender, socioeconomic status, immigration status, personality, and need for cognition. Unlike traditional methods, the Rasch tree does not require pre-specified groups and can handle continuous covariates. Analysis of 4,252 students revealed nine non-predefined nodes, with eleven items showing moderate to large DIF, indicating that combinations of covariates impact test performance.
Original abstract
A key psychometric phenomenon in educational testing is differential item functioning (DIF), which evaluates whether specific test items function differently across subgroups of examinees who have the same level of the underlying (latent) ability. DIF happens when examinees with the same latent trait have different probabilities of correctly responding to a test item, influenced by their subgroup membership. This study aims to illustrate the application of the recursive partitioning Rasch tree model, shortly called the Rasch tree, to examine DIF in a large-scale German reading comprehension test for fifth-grade elementary students across gender, socioeconomic status, immigration status, personality disposition, and the need for cognition. Unlike the conventional DIF detection methods, the Rasch tree does not require a pre-specification of groups for exploring DIF, and continuous covariates can be easily included in the analysis. To investigate DIF of the test, item responses of 4,252 students were analyzed. The Rasch tree analysis generated nine non-predefined nodes, with slightly different patterns of item difficulties. Eleven items were flagged as exhibiting moderate and large DIF in the four splitting nodes. No splits were produced based on the need for cognition. The results indicated that the combination of the covariates impacted students’ test performance.