I am running a multiple linear regression using SPSS to test the effect of ethnicity and ethnic/racial attitudes or perceptions on political predispositions.
One model, as an example, looks like this:
DV: 'support for democracy' (1-7 scale) Controls: age, gender, education etc. Predictors: Ethnicity, and various attitude/perception vars
My question is what to do with these attitude/perception variables when they are correlated; i.e. whether to leave all of them in the model, or remove one or more. A particular case is the pair of variables 'common national culture' ('Bolivians share many common values that unite us as a nation; 1-7 disagree-agree scale) and 'strength of national identification' ('To what extent do you identify as a Bolivian citizen?'; 1-7 scale).
As you might expect, 'common national culture' and 'strength of national ID' are correlated (Pearson coefficient=.268 and is significant). But they also both have significant coefficients in the multiple regression, and adjusted R-squared for the model decreases substantially when either one is removed. In this case - i.e. when both correlated vars have significant coefficients -, should both be kept in?
Many thanks in advance.
VIF is between 1.2-1.4 for each.
Condition indices have values of around 12, but there are 15 variables in the model, so perhaps this is not so remarkable? Looking through the variance proportions, however, neither have any values >0.2