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Dear Michiel,
I am extremely thankful for your regular help. I have a small doubt regarding dominant alternatives in the choice scenarios.

With the above corrected code, I have prepared different number of choice scenarios such as 24, 36 and 48 in total with 6 choice sets in each block. Although I ...

Dear Michiel,
Thank you so much for your prompt replies. As per your suggestions, I am correcting my code as below:

design
;alts = Alternative1*, Alternative2*
;rows = 48
;eff = (mnl,d)
;block = 8
;model:
U(Alternative1) = a.dummy[-0.002|-0.0012|-0.001] * A[0,1,2,3] +
b.dummy[-0.002|-0.0012|-0.001 ...

Dear Michiel,
Thank you so much for your insightful suggestions.

As suggested, I tried dummy coding all the 7 variables (using modified code1 below). I got a doubt which might sound silly. To show that the utility of an alternative is inversely proportional to particular numerical variables such ...

Dear Michiel,
Thank you so much for your valuable suggestions.

When I increased the prior coefficients of numerical variables from 0.001 to 0.01, the pairwise combinations for 2 unlabelled alternatives remained the same (i.e., only first-last levels and second-third levels as pairs). But, when ...

Dear Michiel,
Thank you so much for your valuable suggestions. I have a few more doubts regarding the design of unlabelled experiment for my study.
When I dummy-coded all 7 variables, the d-error increased from 0.19 (when numericals were not dummy-coded) to 0.40. To reduce the error, I tried to ...

Dear all,
This is my basic code. Because priors are not known, I started with zero priors. For the same design, when * and mfederov algorithms are used at a time, there was no attribute-level balance but different possible combinations were found to be present. When mfederov algorithm is not used ...