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

TLDR

Multicollinearity can undermine regression analyses, yet its assessment is rarely reported, and the variance inflation factor is a commonly used diagnostic. The article aims to discuss and demonstrate several post‑hoc methods for assessing multicollinearity. The authors outline a post‑hoc VIF method that derives the factor from standardized coefficients and semi‑partial correlations obtainable from typical regression outputs, and illustrate it with three published‑data examples. The study concludes with a discussion of practical implications.

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.