Modeling DIF in Complex Response Data Using Test Design Strategies

Abstract

This study introduces an approach for modeling multidimensional response data with construct-relevant group and domain factors. The item level parameter estimation process is extended to incorporate the refined effects of test dimension and group factors. Differences in item performances over groups are evaluated, distinguishing two levels of differential item functioning (DIF): a domain level and an item level.

An illustration is presented using a Dutch spelling proficiency scale administered to two subgroups. DIF is modeled by the interaction between group and item domain (domain level DIF) and by the interaction between groups and items within each domain (item level DIF). A set of item response theory models was estimated using an adaptation of the logistic regression approach. The model with domain specific item-by-group interactions or DIF performed better than the other models neglecting domain or group differences.

The method appears to be promising in that explicit domain factors can be implemented into model estimation procedure to better understand why items favor a specific language group over another.