What is Multicollinearity in Multiple Regression?

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What is Multicollinearity?

     Multicollinearity occurs when variables are so highly correlated with each other that it is difficult to come up with reliable estimates of their individual regression coefficients. When two variables are highly correlated, they are basically measuring the same phenomenon or construct. In other words, when two variables are highly correlated, they both convey essentially the same information.


     One way to asses the possibility of multicollinearity among your study variables is to perform correlations. If a correlation coefficient matrix demonstrates correlations of .75 or higher among your variables, there may be multicollinearity. Other statisticians suggest that correlations of .90 or greater may indicate multicollinearity. If this is unclear, request a free research consultation today to receive help with evaluating your multiple regression for multicollinearity. Learn More About Multicollinearity


Let Us Help You Identify & Eliminate Multicollinearity

     Since multicollinearity can adversely affect the results of your multiple regression, it is important that your analyses are properly evaluated to determine the presence of multicollinearity. We can assess for the presence of multicollinearity in your study and provide methods for correcting or reducing its influence. In addition, if you are unsure what statistical test might be the most appropriate and powerful for your study, let us review your research question to help you choose the best statistical test for your study or research project. Request Statistics Help Today!