Most research papers consider a VIF (Variance Inflation Factor) > 10 as an indicator of multicollinearity, but some choose a more conservative threshold of 5 or even 2.5. So what threshold should YOU choose?

In statistics, the variance inflation factor (VIF) is the ratio of the variance of a parameter estimate when fitting a full model that includes other parameters to the variance of the parameter estimate if the model is fit with only the parameter on its own. [1]

Jun 27, 2024 · What Is a Variance Inflation Factor (VIF)? A variance inflation factor (VIF) is a measure of the amount of multicollinearity in regression analysis.

Mar 10, 2019 · The most common way to detect multicollinearity is by using the variance inflation factor (VIF), which measures the correlation and strength of correlation between the predictor variables in a regression model.

Mar 4, 2025 · A VIF of 1 means no correlation with other predictors, while higher values signal potential issues. Although a “rule of thumb” often says VIF > 10 signals serious multicollinearity, different fields may adopt different thresholds depending on the costs of …

Dec 6, 2020 · Variance Inflation Factors (VIFs) measure the correlation among independent variables in least squares regression models. Statisticians refer to this type of correlation as multicollinearity. Excessive multicollinearity can cause problems for regression models.

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Nov 18, 2024 · The variance inflation factor (VIF) is a great tool for detecting multicollinearity, offering insights beyond simple pairwise correlations. This tutorial explains how VIF works, how to calculate and interpret it, and what to do if you find high VIF values.

As the name suggests, a variance inflation factor (VIF) quantifies how much the variance is inflated. But what variance? Recall that we learned previously that the standard errors — and hence the variances — of the estimated coefficients are inflated when multicollinearity exists.