Open Journal of Statistics· 2015
Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis
- 1,517citations
- 2015year
Short summary
A suppressor variable should only be included in regression analysis if its Variance Inflation Factor (VIF) is less than five, establishing suppression as a function of multicollinearity.
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Key points
- Suppression effect in multiple regression may be more common than currently recognized.
- Suppression effect is a function of multicollinearity.
- A suppressor variable should only be included in regression analysis if its Variance Inflation Factor (VIF) is less than five.
AI-generated from the title and abstract; the full text is not read.
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).
The authors' abstract, as published at the source. Open Journal of Statistics, 2015 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics