Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis

Michael Olusegun Akinwande^*, Hussaini Garba Dikko, Agboola Samson

Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria.

DOI: 10.4236/ojs.2015.57075                

Abstract

Suppression effect in multiple regression analysis may be more common in research than what is currently recognized. We have reviewed several literatures of interest which treats the concept and types of suppressor variables. Also, we have highlighted systematic ways to identify suppression effect in multiple regressions using statistics such as: R², sum of squares, regression weight and comparing zero-order correlations with Variance Inflation Factor (VIF) respectively. We also establish that suppression effect is a function of multicollinearity; however, a suppressor variable should only be allowed in a regression analysis if its VIF is less than five (5).

Keywords

Suppression Effect, Multicollinearity, Variance Inflation Factor (VIF), Regression and Correlation, Stepwise Selection

Conflicts of Interest

The authors declare no conflicts of interest.

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