Common Method Bias in Regression Models With Linear, Quadratic, and Interaction Effects

TLDR

This study examines how common method variance influences parameter estimates in linear, quadratic, and interaction regression models. CMV can inflate or deflate bivariate linear relationships, generally diminishes multivariate linear effects when more CMV‑affected predictors are added, and severely deflates quadratic and interaction terms, making them harder to detect.

Abstract

This research analyzes the effects of common method variance (CMV) on parameter estimates in bivariate linear, multivariate linear, quadratic, and interaction regression models. The authors demonstrate that CMV can either inflate or deflate bivariate linear relationships, depending on the degree of symmetry with which CMV affects the observed measures. With respect to multivariate linear relationships, they show that common method bias generally decreases when additional independent variables suffering from CMV are included in a regression equation. Finally, they demonstrate that quadratic and interaction effects cannot be artifacts of CMV. On the contrary, both quadratic and interaction terms can be severely deflated through CMV, making them more difficult to detect through statistical means.