MIMIC approach to assessing differential item functioning with control of extreme response style

Abstract

Likert or rating scales may elicit an extreme response style (ERS), which means that responses to scales do not reflect the ability that is meant to be measured. Research has shown that the presence of ERS could lead to biased scores and thus influence the accuracy of differential item functioning (DIF) detection. In this study, a new method under the multiple-indicators multiple-causes (MIMIC) framework is proposed as a means to eliminate the impact of ERS in DIF detection. The findings from a series of simulations showed that a difference in ERS between groups caused inflated false-positive rates and deflated true-positive rates in DIF detection when ERS was not taken into account. The modified MIMIC model, as compared to conventional MIMIC, logistic discriminant function analysis, ordinal logistic regression, and their extensions, could control false-positive rates across situations and yielded trustworthy true-positive rates. An empirical example from a study of Chinese marital resilience was analyzed to demonstrate the proposed model.