Suppression in regression
Channel: Mikko Rönkkö
Date: 2019-07-18

The video explains the suppression effect in regression analysis The suppression effect occurs when the sign of an effect between two variables differs in a correlation table and a regression table. For instance, a positive correlation between physician age and patient satisfaction in the correlation table becomes negative in the regression results. This phenomenon is described as the suppression effect, and it's emphasized that its technical definition is less important than understanding its occurrence in regression analysis.

The video further examines a case study from Hekman's paper, where physician age shows a positive correlation with patient satisfaction, but a negative regression coefficient. This is attributed to the different things quantified by correlation and regression coefficients: correlation coefficients measure linear associations, which can be causal or spurious, while regression coefficients ideally quantify causal relationships. The video concludes by analyzing the relationship between physician age, tenure, and patient satisfaction, showing that experience (tenure) correlates strongly with age and positively impacts patient satisfaction. This relationship helps explain the observed suppression effect, underscoring that it is a feature of regression analysis rather than a problem.

Slides: https://osf.io/f59ap