Vif Spss. As a second check the variance inflation factor VIF was used with the final model to verify that multicollinearity was not significantly influencing model coefficients 34 35. Another statistic sometimes used for multicollinearity is the Variance Inflation Factor which is just the reciprocal of the tolerance statistics.
There are 2 ways in checking for multicollinearity in SPSS and that is through Tolerance and VIF. A VIF of greater than 5 is generally considered evidence of multicollinearity. Variance Inflation Factor VIF In multiple regression the variance inflation factor VIF is used as an indicator of multicollinearity.
Dimensions 6 and 7 show a condition index above 15.
Several eigenvalues are close to 0 indicating that the predictors are highly intercorrelated and that small changes in the data values may lead to large changes in the estimates of the coefficients. If you divide 1 by669 youll get 1495 which is exactly the same as the VIF statistic shown above. Making process in Multicollinearity Test If the VIF value lies between 1-10 then there is no multicollinearity. Variance inflation factors VIF measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related.
