# Coefficient changes sign when adding a variable in logistic regression

In my logistic regression the sign of coefficients of a variable (location distance of an amenity) changes based on other variables (with time -ve, with travel distance +ve) in the model. When the location distance is the only variable in the model, it has +ve sign.

• Should the variable need to maintain the +ve sign no matter what other variables are added in the model?
• Does changing sign signify a multicollinearity issue? Some IVs are gaining significance while in a bivariate model, they didn't show significance and vice versa.
• Is it okay to add variables that don't have much significance (ex: travel distance has a significance of 0.33 individually, but 0.05 when added with other variables) but becomes significant in the model? thanks.
• I happens for the same reason it happens in ordinary multiple regression. See the diagram here. There will be some collinearity among the variables, but it can be quite mild. – Glen_b Dec 16 '13 at 20:09
• Simpson's paradox is explained here. stats.stackexchange.com/questions/78255/… – Sycorax Dec 16 '13 at 20:11
• You have included the tag [multivariate-regression] & state "I have input it into a multivariate regression". Do you mean that you reran the model w/ >1 predictor variable, or reran the model w/ >1 response / dependent model? Note that >1 predictor, but only 1 response variable is multiple logistic regression. Multivariate LR is when there are >1 response variable. If your situation is actually the former (which I suspect), please edit to clarify & change the tag. – gung - Reinstate Monica Jan 12 '14 at 22:50
• If your situation is multiple LR, then Simpson's Paradox is what's going on. To help understand the idea of the inclusion / exclusion of a confounding variable changing the sign of a focal variable, it may help you to read my answer here: Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression? Although that answer is focused on a different question, & written in the context of linear regression, it illustrates confounding in a way that may make it intuitively accessible. – gung - Reinstate Monica Jan 12 '14 at 22:59
• – behzad.nouri Sep 25 '14 at 21:52