# How to deal with failing the proportional odds assumption in ordinal logistic regression

I am attempting to do ordinal logistic regression but I keep failing to pass the proportional odds assumption. Almost all of my features are shown to have high significance, but the only model that I can fit that passes the Chi-Squared test for proportional odds is rather trivial.

What is the typical way of rectifying this? Is it like linear regression where I can add more interactions or higher order terms to rectify this? If so, how do I go about finding (visually) the ideal adjustments to make to my model? (Like I could plot the residuals against a predictor in linear regression to determine where linearity fails. Is there an equivalent for ORL?)

• Maybe you could think using multivariate regression instead. Commented Apr 21, 2016 at 10:03
• Please define the test for prop. odds. If it is the test developed by Bercedis Peterson that is used in SAS, she showed that test to be anti-conservative. Also provide the distribution of Y in your dataset. If you don't convert to another ordinal model, you can "fix" the model using Peterson's partial proportional odds model. You can't add just any old interaction term. Interactions are with Y. Commented Apr 21, 2016 at 10:37
• @FrankHarrell Thank you very much for the response. It is SAS's test. I'm not sure I can utilize the partial proportional odds assumption though. I am using the SURVEYLOGISTIC procedure and there doesn't seem to be an UNEQUALSLOPES flag for this procedure. It makes sense that different levels of the response are effected differently by the predictor. One of my problems seems to be that there is a diminishing level of returns as one of my predictors gets high. But I'm concerned with predicting Y, so how would a person do prediction without knowing Y in the first place? Could you please explain? Commented Apr 21, 2016 at 15:31
• The SAS test is the Peterson test. Use graphical assessments of the proportional odds assumption instead of that test. PROC GENMOD can handle partial p.o. model but not sure about survey weights. Commented Apr 21, 2016 at 19:50
• @FrankHarrel I have created plots to assess the assumption and concluded that it was not held. Thanks for the tip on proc genmod though. Commented Apr 21, 2016 at 22:53