Many many thanks in upfront!

Chris

Many many thanks in upfront!

Chris

Another option is multinomial logistic regression which only assumes nominal data. You lose some information, but a number of the problems that exist in ordinal logistic regression do not apply.

http://www.ats.ucla.edu/stat/spss/output/ologit.htm

Allison cautions that this test (at least in SAS) tends to reject the null (which means the assumption is violated) more often than it should. And things like adding or deleting variables or respecifying variables can change the result of this test as well. Which is a pain...

To correct something I said earlier, ordinal regression is not harder to interpret as the number of levels goes up - but you need more cases to estimate the model correctly (allison says 10 per level of the DV ignoring the issue of how much data you need generally to correctly use logistic regression).

Its actually multinominal regression that is harder to interpret as the number of levels increase although it does not have the assumption of proportional odds