As a higher VIF indicates, multiple collinearities are present within this data. The analyst resolves the multiple collinearities by removing the initial predictor variables, which eliminates the selection bias
Thevariance inflation factor(VIF)quantifies the extent of correlation between one predictor and the other predictors in a model. It is used for diagnosingcollinearity/multicollinearity. Higher values signify that it is difficult to impossible to assess accurately the contribution of predictors to a mod...
To mitigate the issue of multicollinearity,1 the correlation matrix and Variance Inflation Factor (VIF) were considered, resulting in the estimation of various models for each sample. This research generated the estimated models while ensuring that the average and individual values of the VIF remained...
We con- ducted multiple tests to examine the presence of multi- collinearity in all models: the maximum variance inflation factor (VIF) values are 2.55 and 1.96 for the two samples, respectively, suggesting no strong multicollinearity in our models; the largest condition index for each model ...
Among the post estimation tests, the test result from variance inflation factor (VIF) for multicollinearity shows that there is no serious collinearity between the independent variables since the mean VIF value (2.25) was less than five [52]. In addition, the result from the Ramsey test for a...
thought, we should only keep 3 factors in our final model. This makes the final multicollinearityadjusted model look like this: /* After Parallel Analysis */ proc reg data=pchealth; model cholesterolloss = z1 z2 z3 / VIF; title 'Health - Principal Component Regression Adjustment'; run; ...
Next, to explore the possibility of multicollinearity amongst constructs, the Variance Inflation Factor (VIF) value was calculated which was found to be below 5.0, thereby, indicating a lack of multicollinearity (Hair, Ringle & Sarstedt, 2012). Through the Harman one-factor test as well as the...
The variance inflation factor (VIF) for each variable was inspected to examine multicollinearity between variables. All variables with VIF values > 5 were checked and if necessary excluded from further analysis. Second, function “ordi2step” from the vegan package was used for forward ...
Before the regression analysis, we performed a multi-collinearity test and discovered that the VIF statistic for each variable was less than 10, demonstrating that there was no multicollinearity issue. The estimation results of (1), (2) are reported in Table 5. Models 1a and 1b were full-sa...
An additional linear regression analysis of all control beliefs regressed on PBC indicated the existence of multicollinearity of “high living costs” (Tolerance = 0.163, VIF = 6.141). Therefore, we believe that multicollinearity might explain the positive effect of “high living cost” on PBC. In...