# Is alpha cumulated in ordinal regression?

My outcome is an ordinal variable having 4 levels. I did an ordinal regression and get one universal beta-coefficient describing each level change.

If I do 3 logistic regressions on each level change, I would have an alpha-error of 3 * 5% (multiple testing)

My question: Is there such a cumulation of alpha error in ordinal regression models (proportional odds or continuation ratio), too?

If you are using the ordinary parallel forms of proportion odds or CR models, the restrictions imposed on those models (equal slopes assumptions) concentrates the effects into a single parameter if the predictor is linear. There is no extra type I error. This is a form of borrowing information across $Y$ levels.