Modified Logistic Regression Approaches to Eliminating the Impact of Response Styles on DIF Detection in Likert-Type Scales

• Main Authors: Hui-Fang Chen, Kuan-Yu Jin, Wen-Chung Wang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2017-07-01
• Series: Frontiers in Psychology
• Subjects: extreme response styles; logistic regression; likert scale; differential item functioning; mild response style
• Online Access: https://www.frontiersin.org/article/10.3389/fpsyg.2017.01143/full

Extreme response styles (ERS) is prevalent in Likert- or rating-type data but previous research has not well-addressed their impact on differential item functioning (DIF) assessments. This study aimed to fill in the knowledge gap and examined their influence on the performances of logistic regression (LR) approaches in DIF detections, including the ordinal logistic regression (OLR) and the logistic discriminant functional analysis (LDFA). Results indicated that both the standard OLR and LDFA yielded severely inflated false positive rates as the magnitude of the differences in ERS increased between two groups. This study proposed a class of modified LR approaches to eliminating the ERS effect on DIF assessment. These proposed modifications showed satisfactory control of false positive rates when no DIF items existed and yielded a better control of false positive rates and more accurate true positive rates under DIF conditions than the conventional LR approaches did. In conclusion, the proposed modifications are recommended in survey research when there are multiple group or cultural groups.