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?
You shouldn't use stepwise for any kind of model building. Stepwise results have parameters biased away from 0, standard errors and p-values that are too small, models that are too complex - all in ways that are difficult if not impossible to control.
In ordinal regression, as in any other type, the best way to build a model is to use substantive knowledge. Barring that, for the main effects and the interactions, you should look at effect sizes (you can do this effectively with categorical IVs by outputting the predicted value for each combination of IVs).
In some cases, model averaging can be a good solution (especially if your main goal is prediction rather than explanation). If you must use an automatic procedure, use one that penalizes for the complexity of the model (e.g Lasso, LAR).