Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux

Area of Science:

• Statistics
• Social Sciences Research Methods

Background:

• Multiple regression analysis is a common statistical technique.
• Interaction terms (e.g., A × B) are often included to model complex relationships.
• The interpretation of regression coefficients can be challenging with uncentered predictors.

Purpose of the Study:

• To clarify the effects of mean centering predictors in multiple regression.
• To explain how mean centering impacts regression coefficients and model fit.
• To provide guidance on best practices for handling interaction terms.

Main Methods:

• The study focuses on multiple regression models.
• It specifically examines models including predictors A, B, and their interaction term (A × B).
• The core method involves comparing results with and without mean centering of predictors A and B.

Main Results:

• Mean centering predictors A and B prior to computing the interaction term (A × B) enhances the interpretability of regression coefficients.
• The overall model fit, as indicated by R-squared, remains unaffected by the mean centering procedure.
• This demonstrates a clear benefit for understanding the independent contributions of predictors.

Conclusions:

• Mean centering is a valuable technique for improving the clarity of regression analyses involving interaction terms.
• It simplifies coefficient interpretation without compromising the overall predictive power of the model.
• Researchers are encouraged to use mean centering for more straightforward statistical modeling.