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I got it, thank you for your advice!

Dear professor,
I am very new to DCE study.
I launched the pilot study and tried to analyze the pilot data using MNL in Stata to get the priors for Efficient Designs. And I got 22 questionnaires from pilot study.
In my design, I have three policy attributes , one policy effect attribute and one cost ...

With more rows you have more variation in the data and you can estimate more complex utility functions after data collection, such as nonlinear effects or interaction effects. The minimum number of rows needed is (6 parameters)/(2 alternatives - 1) = 6, so 6 rows is enough to estimate the model and ...

The D-error is fine, so I do not see an issue.

While 12 rows is sufficient to estimate your model, you could consider increasing the number of rows. If you do, Ngene may struggle to find choice tasks without dominant alternatives using the default swapping algorithm. You could consider using the ...

Ok, then my answer was correct. You need to impose constraints to create realistic .

With the default swapping algorithm, you can use if-then constraints in the ;cond property.
With the modified Federov algorithm, you can use reject constraints in the ;reject property.

I refer to the manual for ...

Dear professor
I am sorry for my unclear expression. My problem is that after I ran the choice set with the above code, I realized that one of the choice sets doesn't match reality. That is, the levels of policy attributes are all status quo, but the level of policy effect is major improvement ...

Hello,Professor, I'm new here and I'd like to ask a question about D-efficient design:
I currently have five attributes, including three policy attributes(every attribute have two levels:improve and status quo) ,one policy effect(which have two levels:minor improvement and major improvement) and ...