How to deal with different outcomes between pairwise correlations and multiple regression

I have different results from a correlation table and a multiple regression model. I know that it is an effect of multicollinearity because correlations up to $.474$ exist between predictors, but this is normal in the context of my research area and I cannot remove or change any predictor.

Now I want to provide information on which predictors affect dependent variable and how (positively / negatively). So what is more accurate here, correlation or multiple regression?

• Are you referring to pairwise correlations & a multiple regression model that includes all the variables as predictors? – gung Jul 31 '15 at 18:32
• Yes, I use Bivariate correlation and multiple regression with 'enter' method (includes all variables) – Saleh Ahmadov Jul 31 '15 at 18:35