I have a simple question. I'm doing a regression with countries (346 countries). I have a variable that measures level of previous conflict. I rescaled this variable in a variable that goes from 0.0 (minimum) to 1.0 (maximum) (mean 0.16 and SD = 0.23). I found that I need a quadratic and a cubic term of this variable. My dependent variable is attitudes towards abortion at the national level and it goes from 0 to 9. Now I found that my intercept is 1.74, my main term (conflict) is -7.00, my quadratic term is 17.32 and my cubic term is -10.15. How do I interpret this?

These are raw polynomials.

  • $\begingroup$ Has conflict been analysed as a "raw" or an orthogonal polynomial? $\endgroup$ – Ian_Fin Aug 16 '16 at 10:47
  • $\begingroup$ It is a raw one. $\endgroup$ – Jan Modus Aug 16 '16 at 10:48
  • $\begingroup$ Nope, it isn't. Im not using orthogonal polynomials. This output comes from Mplus and not R. $\endgroup$ – Jan Modus Aug 16 '16 at 14:07
  • $\begingroup$ The statistical content remains the same whatever software you're using. If you need software help, please consult our help center for a list of support links. Is there a question that you have which is not answered here, with respect to quadratic regression? Because there are many more possible polynomial models than there are CV users, it would seem that the distinction between second- and third-degree polynomials is not as important as understanding how regression with basis expansion works generally. $\endgroup$ – Sycorax says Reinstate Monica Aug 16 '16 at 14:42
  • $\begingroup$ Your explicit question is well answered in the linked thread. The problem, I suspect, is not w/ the question you actually asked. You should not be using linear regression w/ a response that is 0 to 9. You need to use ordinal logistic regression (& interpret the polynomial coefficients as in the linked thread), then you'll be fine. $\endgroup$ – gung - Reinstate Monica Aug 16 '16 at 16:08