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?

  • $\begingroup$ Are you referring to pairwise correlations & a multiple regression model that includes all the variables as predictors? $\endgroup$ Jul 31, 2015 at 18:32
  • $\begingroup$ Yes, I use Bivariate correlation and multiple regression with 'enter' method (includes all variables) $\endgroup$ Jul 31, 2015 at 18:35

1 Answer 1


There is no such thing as which is "more accurate" in the abstract. The answer depends on what question you are asking of these data. The correlations tell you about the relationship between each predictor and the dependent variable when you ignore the other variables, and the multiple regression tells you about the relationships when you are controlling for the other variables. They are both correct answers, but to different questions. It may help you to read my answer here: Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?

  • $\begingroup$ I want to know how each of these 6 predictor variables influence 1 dependent variable? All of them are Likert scales. $\endgroup$ Aug 1, 2015 at 16:56
  • $\begingroup$ It just depends on whether you want to know how they influence the DV together or separately. $\endgroup$ Aug 1, 2015 at 17:13
  • $\begingroup$ so when you control for other variables, like in multiple regression model, it basically tells you how each of them influence on DV separately, while pairwise correlation cannot (because of confounding variables I guess)? Please tell me that I got it right. $\endgroup$ Aug 1, 2015 at 17:34
  • $\begingroup$ Multiple regression tells you how each variable influences the DV when controlling for the other variables. It may help to read my linked answer. $\endgroup$ Aug 1, 2015 at 18:46

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