In multiple regression analysis, researchers often include several predictors to explain variation in an outcome. However, problems arise when predictors are highly correlated with each other. This issue is known as multicollinearity. While multicollinearity does not invalidate a regression model, it complicates interpretation and can destabilize coefficient estimates. This article explains what multicollinearity is, why…