I'm having trouble explaining some results...
I have 5 independent variables (A, B, C, D and E) and I want to know their relation to the dependent (Y).
Only variables A and C are significantly positively correlated with Y.
Then, in a multiple regression (model was significant with a high F) only variables D and E were found to be significant predictors.
I’ve read a little bit about suppression and tolerance (tolerance statistics are fine in my case), but I’m unsure how to explain what is happening here.
Do I explain that variables A and C are suppressed in the regression model? Or do I explain that variables D and E were suppressed in the correlations?
For the research question (predictors of Narcissism), in my opinion it would make more sense that higher scores on A (perceived unfairness of childhood discipline) and C (reward orientation) are more closely related to Y (Narcissism) than D (loneliness) and E (feeling socially supported by friends and family).
I’ve read similar questions here and elsewhere but cannot find a specific answer about how to explain where and on which variables suppression occurs.