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Pedhazur, E.J. (1997) Multiple Regression in Behavioral Research: An Explanation and Prediction. Holt, Rinehart & Winston, New York.

has been cited by the following article:

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

  AUTHORS: Michael Olusegun Akinwande, Hussaini Garba Dikko, Agboola Samson

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

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.7, December 24, 2015

  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).