So, I'm having a super-hard time understanding these concepts.
I have created a model with one outcome variable and several predictors. Through the model, I have access to partial and semi-partial correlations, squares of semi-partial correlations for each of my predictor variables, and the multiple R^2 value for the model as whole.
Now, I understand that there is some relationship between these that you can use to ascertain whether or not we are dealing with enhancement or suppression effects, but I'm having a hard time wrapping my head around it, as all the articles I've found have been super-technical. Could anyone explain how I can use these values to study whether some of the predictors are suppressors, enhancers, or redundant?
