P-values vs Coefficient Values in GLM

In the following excerpt, the author noted an association with the dependent variable based solely on the p-value and not the magnitude of the coefficient. Here is a link to the study.

I thought that the coefficients would tell us the magnitude of change in the dependent variable with a change in the feature; like a gradient. Why are Followers and Followees strong predictors if they have such small coefficients?

• Please give a reference to this study in usual academic form or alternatively a stable and accessible URL. – Nick Cox Jan 7 '16 at 0:53
• If I changed a variable from being measured in mm to being measured in km (or from dollars to millions of dollars), the coefficient would change by six orders of magnitude, but the p-value would remain unchanged. – Glen_b Jan 7 '16 at 1:46
• @NickCox I have added the URL. – Duck Jan 7 '16 at 13:54
• Thanks. The .pdf there carries no standard bibliographic details but here they are: Bongwon Suh, Lichan Hong, Peter Pirolli, and Ed H. Chi. 2010. Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network. In Proceedings of the 2010 IEEE Second International Conference on Social Computing (SOCIALCOM '10). IEEE Computer Society, Washington, DC, USA, 177-184. DOI=dx.doi.org/10.1109/SocialCom.2010.33 – Nick Cox Jan 7 '16 at 14:29

• Agreed. I think it's consistent with that to point out that some rescaling of predictors would have been a very good idea in the study cited. The coefficients of four predictors are given to just 1 sig. fig. as a side-effect of working with values $>> 1$. While it's perhaps unlikely that any one would want to use this particular equation for comparison, in other fields of science using other studies' results for prediction is important. Indeed it's a criterion of scientific or practical worth if a fitted model is of wider interest. – Nick Cox Jan 7 '16 at 0:51