  A new method for the identification of differential item functioning (DIF) by
using recursive partitioning techniques is proposed. We assume an extension of
the Rasch model that allows for DIF being induced by an arbitrary number of
covariates for each item. Recursive partitioning on the item level results in
one tree for each item and leads to simultaneous selection of items and
variables that induce DIF. For each item it is possible to detect groups of
subjects with different item difficulties, defined by combinations of
characteristics that are not pre-specified. An algorithm is proposed that is
based on permutation tests. Various simulation studies, including the
comparison with traditional approaches to identify items with DIF, show the
applicability and the competitive performance of the method. Two applications
illustrate the usefulness and the advantages of the new method.
