# Ordinal regression with categorical covariates and predictors

I am trying to do an ordinal logistic regression (ordinal outcome variables with more than 2 categories) with nominal (more than 2 categories for some) predictor variables, as well as nominal (more than 2 categories for some) covariates/moderators. First of all, I would like to test for effect interaction to see which of my covariates/moderators are indeed moderators so that I can treat them as such. Second of all, I would like to know what's the best way to come up with the optimal model? Do I need to do dummy variables for the covariates? Can I do a stepwise for ordinal regression?

• I advise against stepwise model selection (you can see my answer here: algorithms-for-automatic-model-selection). In general, I think finding the "optimal model" is overrated. If you want to build a predictive model, you should look into cross validation. Oct 30, 2012 at 21:12

• I don't know SPSS at all. But the parameter estimates are effect sizes. I am sure SPSS lets you output predicted values too, but I don't know how. Manually doing what the computer could do faster doesn't make it better. Oct 30, 2012 at 21:37
• "You shouldn't use stepwise for any kind of model building." - this is an incredibly sweeping statement and it just isn't true. If your goal is prediction then a stepwise selection algorithm may be quite sensible. Yes, stepwise selection destroys the interpretation of $p$-values but inquiries where you care about $p$-values does not subsume all model building. Dogma and unconditional statements like this do not spread good statistical practice. Oct 31, 2012 at 2:20
• @Marco, it is not just that the $p$-values aren't correctly interpreted anymore. Let's assume, as the user asking the question suggests, that we eliminate those predictors that aren't significant. A problem with this is that we are eliminating those predictors for which we've underestimated their significance, and keeping those for which we've overestimated their significance. Dec 30, 2012 at 15:16