Extracting the Variance Inflation Factor and Other Multicollinearity Diagnostics from Typical Regression Results

2017/02/01 by Christopher Glen Thompson, Christopher G. Thompson, Rae Seon Kim +2 · 8 citations
Chemistry · Computer Science · Mathematics · #Advanced Statistical Methods and Models #Computational Drug Discovery Methods #Spectroscopy and Chemometric Analyses

paper · doi:10.1080/01973533.2016.1277529

openalex publication_date 2017/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Multicollinearity is a potential problem in all regression analyses. However, the examination of multicollinearity is rarely reported in primary studies. In this article we discuss and show several post hoc methods for assessing multicollinearity. One such multicollinearity diagnostic is the variance inflation factor. We outline the post hoc variance inflation factor method, which computes the variance inflation factor from the standardized regression coefficient and semi-partial correlation, both of which can be calculated from commonly reported regression results. Three examples of computing multicollinearity diagnostics using data from published studies are shown. We conclude with a discussion and practical implications.

Citations

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• A Caution Regarding Rules of Thumb for Variance Inflation Factors
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