Gologit2 for ordinal outcome

If you have an ordinal outcome /variable ('at risk' drinking, 'alcohol dependent' in a alcohol scale)

If you want to argue that the probabilities of level 1, 2, 3, …n of the outcome are cumulative, (for example, people with increased chance in 'at risk' category have increased chance in 'dependence' category)

If you are trying to adjust for problems caused by nature of a survey (sample does not represent the population due to non-response, finite population correction),

If you want test the proportional odds assumption for your ordinal variable,


 

1. Install gologit2 into stata
   
2. Key in
   
   
   gologit2 depvar invar list, autofit svy subpop(group var)
   

If the test for proportional odds assumption is insignificant, your model is ok.

If it is not, use separated standard logistic equations, or, you can try multinomial logistic regression