  This paper proposes a method for assessing differential item functioning
(DIF) in item response theory (IRT) models. The method does not require
pre-specification of anchor items, which is its main virtue. It is developed in
two main steps, first by showing how DIF can be re-formulated as a problem of
outlier detection in IRT-based scaling, then tackling the latter using methods
from robust statistics. The proposal is a redescending M-estimator of IRT
scaling parameters that is tuned to flag items with DIF at the desired
asymptotic Type I Error rate. Theoretical results describe the efficiency of
the estimator in the absence of DIF and its robustness in the presence of DIF.
Simulation studies show that the proposed method compares favorably to
currently available approaches for DIF detection, and a real data example
illustrates its application in a research context where pre-specification of
anchor items is infeasible. The focus of the paper is the two-parameter
logistic model in two independent groups, with extensions to other settings
considered in the conclusion.
