Hi everyone,
I am working on a logistic regression model with five independent, continuous key variables: authoritarianism, nationalism, social conservatism, religiosity and anti-immigrants attitudes + controls, which are used to predict the preference for authoritarian populist parties. These variables are indexes made of items combined with
Code:
rowmean
.
I am having troubles with fitting a model for robustness test with interaction terms among these variables. For instance, when using the code
Code:
logit c.nationalism##c.anti-immigrants socialconservatism religiosity authoritarianism
I obtain negative coefficients for the interaction terms. This might not seem to be a problem, however all the results are highly counter-intuitive and I obtain negative coefficients for the interaction terms for any variable combination, across the four countries that I am analysing. However, if for instance I use the code
logit c.nationalism#c.anti-immigrants socialconservatism religiosity authoritarianism
the results are more in line with the theory and also with the correlation matrix of the variables. I already tried to centre the variables and to rescale them, but with no success. As I need to include both the variables and the interaction terms in the model as in the code
Code:
logit c.nationalism##c.anti-immigrants socialconservatism religiosity authoritarianism
I was wondering if you have any suggestions on how I could solve this or why this is happening.
Thank you very much