For linear regression, I understand that there is a 1 in 10 rule. For example, if I have a continuous dependent variable and 100 observations then at most I could add 10 binary or continuous independent variables in the multivariable model.
For logistic regression, if I have a nominal dependent variable such as eye colour: blue (N=50), green(N=30) and brown(N=20), then I could at most have 2 independent variables in the model, i.e. the number of observations in the category with the lowest N divided by 10(i.e. 20/10=2).
For ordinal logistic regression(proportional odds), for example, the ordinal dependent variable is Tobacco use frequency: daily(N=50), regularly(N=30) and occasionally or no use(N=20). I wonder how many independent variables could be included? Is it the same as logistic regression with nominal outcomes? e.g. 20/10=2?
Some other questions are: What would be the consequences if I over fitted a ordinal logistic model? Is there a method that I could test the overfitting quantitatively? Would there be a difference in sample size requirements between an explanatory ordinal logistic regression and a predictive ordinal logistic regression?
Any help would be much appreciated!