Different Approaches to Differential Item Functioning in Health Applications

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Abstract
Reviewed in this article are topics related to the study of invariance and differential item functioning (DIF) that have received relatively little attention in the literature. Several factors influence DIF detection; these include (1) model fit, (2) model assumptions, (3) disability distributions, (4) purification, (5) cutoff values for magnitude measures, and (6) sample and scale size. Approaches to DIF detection are discussed in terms of model assumptions, purification, magnitude and impact, and possible advantages and disadvantages of each method. An integrated approach to the examination of measurement equivalence, invariance, and DIF is necessary for measurement in an increasingly multi-ethnic society. Ideally, qualitative analyses should be performed in an iterative fashion to inform about findings of DIF. However, if an already-developed measure is being evaluated, then the steps might be to focus first on dimensional invariance using factor analytic methods, followed by DIF analyses examining both significance and magnitude of DIF, accompanied by formal tests of the impact of DIF. The DIF analytic method selected in the second step might be determined based on the findings summarized in the table presented within this paper.