As for cumulative link models (also called ordinal logistic regression models), Christensen (2016) reports that, in the case the proportional odds assumption is not met, one or more covariates need to be scaled to relax the assumption.
However, when visualizing the summary, it may happen that the non-scaled covariate results as non-significant while the same scaled covariate as significant. What is the meaning of that?
Thanks in advance,
Marcello
As far as I understood, cumulative link models and ordinal logistic regression models are the same. Furthermore, they are driven by the same assumptions:
1. The dependent variable must be ordered.
2. One or more of the independent variables are either continuous, categorical or ordinal.
3. No multi-collinearity.
4. Proportional odds.
Is that right?
BTW here's a set of notes that clarify things Best wishes David Booth
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