Extreme and non-extreme response styles are prevalent in survey research using Likert-type scales. Their effects on measurement invariance (MI) in the context of confirmatory factor analysis are systematically investigated here via a Monte Carlo simulation study. Using the parameter estimates obtained from analyzing a 2007 TIMSS data set, a population model was constructed. Original and contaminated data with one of two response styles were generated and analyzed via multi-group confirmatory factor analysis with different constraints of MI. The results indicated that the detrimental effects of response style on MI have been underestimated. More specifically, these two response styles had a substantially negative impact on both model fit and parameter recovery, suggesting that the lack of MI between groups may have been caused by the response styles, not the measured factors of focal interest. Practical implications are provided to help practitioners to detect RSs and determine whether RSs is a serious threat to MI.