Interpretation problems for 9 categories of response variable in ordinal and probit regression I have 9 categories of response variable, and facing interpretation problems. 


*

*Could I use this ordinal data as continuous data?

*If not then please refer me to some example with more than 5
categories.


Thanks!
 A: As Macro has pointed out correctly, the interpretation of an ordered probit or logit model does not depend on the number of categories of the dependent variable. However, there might be some issues with the reporting of the results. If you have a dependent variable with 9 categories and you want to present outcome-specific marginal effect for each of your explanatory variables in detail, the whole thing becomes very cumbersome. In that case, you have to be a bit inventive. You could for instance rely on the graphical methods that have been advised elsewhere.  
Another possibility would be to reduce the number of categories. Does it make sense to keep nine categories, or could some categories be pooled? 
A: Technically, ordinal is ordinal and continuous is continuous, and you shouldn't treat one as the other. However, in practical terms, it gets a bit blurry somewhere. Is your ordinal variable really continuous "underneath"? That is, are the nine levels really just points on a continuous scale? If they are, then one thing you could do is run both ordinal and OLS regression and see if the results are very different. If they are not, you can go with the OLS results and note what you did. Perhaps not exactly technically correct, but "all models are wrong and some models are useful".
