Calculating the Relative Importance of Multiple Regression Predictor Variables Using Dominance Analysis and Random Forests

Researchers often make claims regarding the importance of predictor variables in multiple regression analysis by comparing standardized regression coefficients (standardized beta coefficients). This practice has been criticized as a misuse of multiple regression analysis. As a remedy, I highlight the use of dominance analysis and random forests, a machine learning technique, in this method showcase article for accurately determining predictor importance in multiple regression analysis. To demonstrate the utility of dominance analysis and random forests, I reproduced the results of an empirical study and applied these analytical procedures. The results reconfirmed that multiple regression analysis should always be accompanied by dominance analysis and random forests to identify the unique contribution of individual predictors while considering correlations among predictors. I also introduce a web application for facilitating the use of dominance analysis and random forests among second language researchers.