What can the reasons be that the assumption of proportional odds / assumption of parallel regression lines is not met?
By using MASS::polr
in R, I tried to run different models and change variables, but the brant test shows non-proportionality persistently.
How can I study and find out what may cause this? I looked at the correlationsplot and vif, the variables are not correlated and there is no multicollinearity using the vif/tolerance method.
Using boxplot(df$var1, plot = TRUE)$out
and length(boxplot(df$var1)$out)
I can see that there are outliers.. however there are too many for what I will say that they are outliers.
Even though I decide to remove the outliers there are many outliers in the dependent variable which is ordinal discrete and the same for the independent variables. Meaning that if category 1 in the dependent variable is outlier and the count of it is 100, I don't see it as a good idea to remove them or replace with NA. Some variables have 10 outliers, others have more than 200.
What are other methods you can suggest? I'm not asking what to do when the parallel regression assumption is not met but how I can find out what cause this violation.