When predictor variables in a study are highly correlated, it can cause issues known as multicollinearity. This can make the results less reliable, with coefficients that don't make sense or change a lot with small changes in the data. One way to spot multicollinearity is if the variables have high correlations above 0.8 or 0.9. A variance inflation factor (VIF) of 10 or more can also indicate a problem. To deal with multicollinearity, researchers can combine or remove variables, or adjust coefficients in a logical way. Techniques like ridge regression and structural equation modeling can help improve the accuracy of the results. Ultimately, careful model development and data collection are key to avoiding issues with multicollinearity.