Can anyone explain the paradox of observing a non-significant Pearson correlation although that independent variable should be included in the Regression model?

  • $\begingroup$ Welcome to CV. Since you’re new here, you may want to take our tour, which has information for new users. Why do you think there is a paradox? $\endgroup$
    – T.E.G.
    Apr 9, 2018 at 21:55
  • $\begingroup$ Thanks for your reply. I would just like to clarify if one predictor is non-significant in the Pearson correlation but significant in the Regression Model, what does this mean? Aren't these result contradicting? $\endgroup$
    – Rachel
    Apr 9, 2018 at 22:01
  • $\begingroup$ It might be better if you provide more information. Is there only one predictor in linear regression as well? And in fact, is this a linear regression model? $\endgroup$
    – T.E.G.
    Apr 9, 2018 at 22:03
  • $\begingroup$ Its a multiple regression, one DV and 5 predictors. The multiple regression shows that one of the predictor is significant in this model but non-signficiant for pearsons. What are the implications of these results? $\endgroup$
    – Rachel
    Apr 9, 2018 at 22:06
  • 1
    $\begingroup$ Among many other reasons (example multicollinearity), one common reason is if variance of the predictor variable is too high compared to the variance of dependent variable. $\endgroup$
    – uday
    Apr 9, 2018 at 22:40


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