I have a categorical response (ordinal, lets call it A) collected from a complete randomized block design experiment where the explanatory variable is treatment. How do I account for the between block variation? I have found models that do this for dichotomous response variables but my response has >2 levels, treatment is also categorical and has 10 levels and there are 8 separate blocks. Could I hypothetically run a logistic regression and add the separate block factors to account for the variation?
The treatment variable has 10 levels but I could separate them so that I compare them 2 at a time, control vs some treatment. Would it be reasonable to run a GLMM model using treatment as a response? So that level changes in the categorical variable, A, affect the odds of having received treatment or is there some fallacy in this?