Comparing Multiple Regression, Path Analysis, and Dominance Analysis with Correlated Predictor Variables

Predictors variables are often highly correlated. Data from 585 Latino adolescents were used to compare results from OLS regression, path analysis, and dominance analysis. Multicollinearity resulted in observable suppression effects in regression and path analysis; thus, dominance analysis should be considered as a viable approach when predictors are highly correlated.