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# How to Test for Multicollinearity in SPSS

MulticollinearityÂ inÂ regression analysisÂ occurs when two or more predictor variables are highly correlated to each other, such that they do not provide unique or independent information in the regression model. If the degree of correlation is high enough between variables, it can cause problems when fitting and interpreting the regression model.Â

One way to detect multicollinearity is by using a metric known as theÂ variance inflation factor (VIF), which measures the correlation and strength of correlation between the predictor variables in a regression model.

This tutorial explains how to use VIF to detect multicollinearity in a regression analysis in SPSS.

### Example: Multicollinearity in SPSS

Suppose we have the following dataset that shows the exam score of 10 students along with the number of hours they spent studying, the number of prep exams they took, and their current grade in the course:

We would like to perform a linear regression usingÂ scoreÂ as the response variable andÂ hours,Â prep_exams, andÂ current_grade as the predictor variables, but we want to make sure that the three predictor variables arenâ€™t highly correlated.

To determine if multicollinearity is a problem, we can produce VIF values for each of the predictor variables.

To do so, click on theÂ AnalyzeÂ tab, thenÂ Regression, thenÂ Linear:

In the new window that pops up, dragÂ scoreÂ into the box labelled Dependent and drag the three predictor variables into the box labelledÂ Independent(s). Then clickÂ StatisticsÂ and make sure the box is checked next toÂ Collinearity diagnostics. Then clickÂ Continue. Then clickÂ OK.

Once you clickÂ OK, the following table will be displayed that shows the VIF value for each predictor variable:

The VIF values for each of the predictor variables are as follows:

• hours: 1.169
• prep_exams: 1.403