huge differences in R2 between OLS and ordered logit regression model


New Member
I was wondering why I get huge differences in R2 when running a ordered logit model and a OLS model. I use the same DV and IVs for both models. The DV has values between 0-16. IVs are 5- point likert scale and dummy-variables.
For the ordered logit odel, STATA gives me a R2<1%, whereas the OLS model indicates an R2 of 26%.

Tanks in advance.


Fortran must die
The pseudo R2 of logistic regression have nothing in common with the R2 in OLS. They aren't calculated the same way and don't mean the same thing (there is no real logistic regression R squared equivalent of explained variance which is what r squared means in OLS). Also there are dozens of pseudo R squared in logistic regression and they are calculated differently and mean different things (often far different).

It is doubtful whether it is even of value to use the logistic regression R squared. It was created because researchers liked having, or practisioners did, something like the OLS R squared. But it probably causes more confusion than good IMHO because people assume it means explained variance and it does not. Nor can researchers agree on how to calculate it.