How do you adjust for multicollinearity?
How to Deal with Multicollinearity
- Remove some of the highly correlated independent variables.
- Linearly combine the independent variables, such as adding them together.
- Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
What is acceptable multicollinearity?
Multicollinearity was measured by variance inflation factors (VIF) and tolerance. If VIF value exceeding 4.0, or by tol- erance less than 0.2 then there is a problem with multicollinearity (Hair et al., 2010).
How are variance factors used to detect multicollinearity?
Again, this variance inflation factor tells us that the variance of the weight coefficient is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model. So, what to do? One solution to dealing with multicollinearity is to remove some of the violating predictors from the model.
What happens if multicollinearity is not present in a model?
Therefore, if multicollinearity is not present for the independent variables that you are particularly interested in, you may not need to resolve it. Suppose your model contains the experimental variables of interest and some control variables.
How to reduce structural multicollinearity in regression analysis?
Centering the variables is a simple way to reduce structural multicollinearity. Centering the variables is also known as standardizing the variables by subtracting the mean. This process involves calculating the mean for each continuous independent variable and then subtracting the mean from all observed values of that variable.
What should the VIF be for multicollinearity?
In most cases, there will be some amount of multicollinearity. As a rule of thumb, a VIF of 5 or 10 indicates that the multicollinearity might be problematic. In our example, the VIFs are all very high, indicating that multicollinearity is indeed an issue. After we remove BMI from the model, the VIFs are now very low.