Using Unordered Polytomous Covariates in Rasch Trees for DIF Detection

The Rasch Tree model uses model-based recursive partitioning to detect differential item functioning (DIF) across combinations of covariates. This intersectional approach is a powerful exploratory tool, but it has limitations when incorporating unordered polytomous covariates (e.g. race), particularly surrounding interpretability and computational efficiency. As a result, many studies restrict Rasch Tree analyses to contain only dichotomous or continuous covariates. Recent work has proposed collapsing polytomous covariates into fewer categories prior to model fitting, but the effects of this approach have not been assessed. The present study evaluates the impact of collapsing polytomous covariates on Rasch Tree performance using both simulations and empirical data. We compare DIF detection accuracy and model complexity under the default partitioning algorithm and an alternative approach based on k-means clustering. Results indicate that clustering reduces sensitivity and overall DIF detection accuracy. In addition, detection accuracy for polytomous covariates remains low even under the default procedure, highlighting the need for improved methods for incorporating such covariates in Rasch Tree models.