Collinearity, Power, and Interpretation of Multiple Regression Analysis

TLDR

Multiple regression is widely used in marketing research, yet correlated predictors raise common concerns about collinearity, and existing literature has not clarified when or how severely it affects estimates. The study investigates the conditions under which collinearity influences regression estimates in typical cross‑sectional marketing research. Results indicate that fears of harmful collinearity effects are often exaggerated and that its impact must be evaluated together with other factors affecting estimation accuracy.

Abstract

Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research. Yet, correlated predictor variables—and potential collinearity effects—are a common concern in interpretation of regression estimates. Though the literature on ways of coping with collinearity is extensive, relatively little effort has been made to clarify the conditions under which collinearity affects estimates developed with multiple regression analysis—or how pronounced those effects are. The authors report research designed to address these issues. The results show, in many situations typical of published cross-sectional marketing research, that fears about the harmful effects of collinear predictors often are exaggerated. The authors demonstrate that collinearity cannot be viewed in isolation. Rather, the potential deleterious effect of a given level of collinearity should be viewed in conjunction with other factors known to affect estimation accuracy.