In multiple regression, the variance inflation factor (VIF) is used as an indicator of multicollinearity.
Explanation of Variance Inflation Factor
Computationally, it is defined as the reciprocal of tolerance: 1 / (1 – R2). All other things equal, researchers desire lower levels of VIF, as higher levels of VIF are known to affect adversely the results associated with a multiple regression analysis. In fact, the utility of VIF, as distinct from tolerance, is that VIF specifically indicates the magnitude of the inflation in the standard errors associated with a particular beta weight that is due to multicollinearity.