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Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis
Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
- 1 Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
- 2 Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
- 3 Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Open Journal of Statistics·Volume 05 (2015)·Pages 754–767·Published 11 December 2015·DOI10.4236/ojs.2015.57075
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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: R<sup>2</sup>, 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).
KeywordsSuppression EffectMulticollinearityVariance Inflation Factor (VIF)Regression and CorrelationStepwise Selection
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