I have cross-sectional data for the Diabetes group. I am trying to test a model the predicts a specific complication “ Cataract”(i.e., a dependent variable which is an ordinal variable) by other health complications i.e., independent variables which could be nominal, ordinal, and scale variables.
As the data is cross-sectional, in this case, I think that I don’t need to use any code to prepare the data for analysis, I mean I should use the below code:

Code:
 
ge id= _n
encode patient, gen(PATIENT)
The first question:
I have an independent variable that consists of two groups in the same column, where the first group named type 1 while the second group named type 2. So, in this case: what is the code to test that:

The second question:
Let us assume that the model that I want to use is the following model, and please remember that my main aim is to predict a specific complication “ Cataract”. So, the model is:
Code:
 Cataract= Gender + Age + Diabetes duration
Where the Cataract is ordinal variable.
Gender is nominal variable
Age is ordinal
Diabetes duration is scale.

So, I think that I should use the ordinal logistic regression, and the code I think for this will be:
Code:
Ologit  Cataract Gender  Age  Diabetes_duration, r
But I need to see the relationship between each of the independent variable with the dependent variable, for instance, the effect of Gender on all levels of Cataract, so I think the code will be:

Code:
margins, dydx ( Gender)
marginsplot
So could you please correct me if any of the above codes are not correct?

The third question:
I am very interested in finding whether the component of gender I mean male and female have different predictions on the dependent variable or not, if yes could you please tell me the code.

Many thanks in advance