Variance Inflation Factor

Variance inflation factor is a measure of the amount of multicollinearity in a set of multiple regression variables. A multiple regression is used when a person wants to test the effect of multiple variables on a particular outcome. The dependent variable is the outcome that is being acted upon by the independent variables, which are the inputs into the model. Multicollinearity exists when there is a linear relationship, or correlation, between one or more of the independent variables or inputs. Multicollinearity creates a problem in the multiple regression because since the inputs are all influencing each other, they are not actually independent and it is difficult to test how much the combination of the independent variables affects the dependent variable, or outcome, within the regression model.

To ensure the model is properly specified and functioning correctly, there are tests that can be run for multicollinearity. Variance inflation factor is one such measuring tool. Using variance inflation factors helps to identify the severity of any multicollinearity issues so that the model can be adjusted. Variance inflation factor measures how much the behavior (variance) of an independent variable is influenced, or inflated, by its interaction/correlation with the other independent variables.

The variance inflation factor is commonly used with an ordinary least squares regression. It measures the extent of multicollinearty within the model. Multicollinearity reduces a model's legitimacy and predictive power. Variance inflation factors allow a quick measure of how much a variable is contributing to the standard error in the regression. When significant multicollinearity issues exist, the variance inflation factor will be very large for the variables involved. After these variables are identified, several approaches can be used to eliminate or combine collinear variables, resolving the multicollinearity issue.