  Various methods to detect differential item functioning (DIF) in item
response models are available. However, most of the methods assume that the
responses are binary, for ordered response categories available methods are
scarce. In the present paper DIF in the widely used partial credit model is
investigated. An item-focussed tree is proposed that allows to detect
DIF-items, which might affect the performance of the partial credit model. The
method uses tree methodology yielding a tree for each item that is detected as
DIF-item. The resulting trees show which variables induce DIF and in which way.
The visualization as trees makes the results easily accessible. The method is
compared to an alternative approach, simulations demonstrate the performance of
the method and an application illustrates how it works for real data.
