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A Robust Method for Detecting Item Misfit in Large Scale Assessments

Viable methods for the identification of item misfit or Differential Item Functioning (DIF) are central to scale construction and sound measurement. Many approaches rely on the derivation of a limiting distribution under the assumption that a certain model fits the data perfectly. Typical assumptions such as the monotonicity and population independence of item functions are present even in classical test theory but are more explicitly stated when using item response theory or other latent variable models for the assessment of item fit. The work presented here provides an alternative approach that does not assume perfect model data fit, but rather uses Tukey’s concept of contaminated distributions and proposes an application of robust outlier detection in order to flag items for which adequate model data fit cannot be established.