Extreme response style (ERS) is prevalent in Likert- or rating-type data, but previous research has not well addressed its impact on differential item functioning (DIF) assessments. This study aimed to fill in the knowledge gap and examined the influence of ERS on the performance of the multiple indicators multiple causes (MIMIC) framework and logistic regression (LR) approaches in DIF detections, including the ordinal logistic regression (OLR) and the logistic discriminant functional analysis (LDFA). The findings from a series of simulations showed that the difference in ERS between groups caused inflated false positive rates and problematic true positive rates in DIF detections when ERS was not taken into account. This study proposed a new class of modified LR and MIMIC approaches to eliminating the ERS effect on DIF assessment. These proposed modifications showed satisfactory control of false positive rates and yielded more accurate true positive rates under varied DIF conditions than the conventional approaches did. An empirical example of Chinese marital resilience was analyzed to demonstrate the proposed models.