Abstract
Test fairness is critical to the validity of group comparisons involving gender, ethnicities, culture, or treatment conditions. Detection of differential item functioning (DIF) is one component of efforts to ensure test fairness. The current study compared four treatments for items that have been identified as showing DIF: deleting, ignoring, multiple-group modeling, and modeling DIF as a secondary dimension. Results of this study provide indications about which approach could be applied for items showing DIF for a wide range of testing environments requiring reliable treatment.
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