Ordinal regression when odds are not proportional

I’m working on a survey study and my data do not satisfy the proportional odds assumption (checked as Ordinal Logistic Regression | R Data Analysis Examples).
I know that there are more complex models which can allow for partial proportionality or non proportionality but I’d like to keep the presentation of my results clear and straightforward the most. What do you think would be the best way to analyze and/or represent my data (I’m ok also with plots)?
I saw some Authors do multiple logistic regressions for every cutpoint in the likert scale.
I have a series of 5 points likert scale questions with 4 demographic characteristic I want to use as covariates.

Thank you!


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There are many ways to look at this excellent question, and thanks for the pointer to the excellent UCLA article. The most cohensive approach is to specify a prior distribution for the amount of non-proportionality through the use of a partial proportional odds model. The R rmsb package blrm function allows you to do this. Short of that, you can either ignore non-proportional odds, knowing that the odds ratio may still be very meaningful, or fit a partial proportional odds model or constrained partial PO model using either Bayesian modeling with blrm or using a frequentist procedure with the VGAM package vglm function.

Analyzing all possible cutpoints would give you the worst of all possible worlds unless the sample size is very large, in which case polytomous logistic regression is the way to put all those together without making any proportionality assumption.