Simplified, I have 17 items that are barriers to helping. Each item is rated on a 5-point Likert scale. I have four outcomes that are different types of helping (3 binary outcomes, 1 continuous). I want to determine the relative importance of these barriers in explaining each of these helping behaviors. I would prefer to compare the barrier items themselves and not factor analyze the items. I have been reading about different variable selection and importance methods (here, here, or here).
I am learning toward a relative importance method (relaimpo/relimp) or one using a random forest method (randomForest, Boruta). Are either of these (or any other variable selection or importance methods) better/more robust given I have 5 point Likert items as predictors?