i have created the following CBC with alternative-specific design:

One primary attribute with two levels

One common attribute with five levels (different prices)

each level of the primary attribute has three conditional attributes:

one conditional attribute with five levels

one conditional attribute with three levels

one conditional attribute with two levels

All in all my design is:

Task generation method is 'Balanced Overlap' using a seed of 1.

Based on 300 version(s).

Includes 3000 total choice tasks (10 per version).

Each choice task includes 3 concepts and 8 attributes.

Question 1: Are there any significant errors/risks up to this point?

The test report shows almost the same freq within each attribute with values close to and well over 1000. But the following warning is displayed:

**Warning: You have specified prohibitions/alternative-specific rules between two or more attributes. You cannot automatically estimate an interaction effect between two attributes when a prohibition or alternative-specific rule is in place.

Question 2: Is this warning common for alternative-specific designs? Or did I make a mistake?

The Advanced Test Design looks like:

Logit Efficiency Test Using Simulated Data

-------------------------------------------------------------

Main Effects: 1 2 3 4 5 6 7 8

Build includes 300 respondents.

Total number of choices in each response category:

Category Number Percent

-----------------------------------------------------

1 847 28.23%

2 868 28.93%

3 865 28.83%

4 420 14.00%

There are 3000 expanded tasks in total, or an average of 10.0 tasks per respondent.

Iter 1 Log-likelihood = -4054.29664 Chi Sq = 209.17289 RLH = 0.25887

Iter 2 Log-likelihood = -4043.37506 Chi Sq = 231.01605 RLH = 0.25981

Iter 3 Log-likelihood = -4042.63996 Chi Sq = 232.48624 RLH = 0.25988

Iter 4 Log-likelihood = -4042.60526 Chi Sq = 232.55565 RLH = 0.25988

Iter 5 Log-likelihood = -4042.60382 Chi Sq = 232.55853 RLH = 0.25988

Iter 6 Log-likelihood = -4042.60376 Chi Sq = 232.55865 RLH = 0.25988

Iter 7 Log-likelihood = -4042.60376 Chi Sq = 232.55866 RLH = 0.25988

*Converged

Std Err Attribute

1 0.02083 1 1

2 0.02083 1 2

3 0.04400 2 1

4 0.04497 2 2

5 0.04414 2 3

6 0.04412 2 4

7 0.04449 2 5

8 0.06481 3 1

9 0.06410 3 2

10 0.06437 3 3

11 0.06445 3 4

12 0.06585 3 5

13 0.04514 4 1

14 0.04509 4 2

15 0.04503 4 3

16 0.06624 5 1

17 0.06423 5 2

18 0.06409 5 3

19 0.06475 5 4

20 0.06376 5 5

21 0.04526 6 1

22 0.04464 6 2

23 0.04447 6 3

24 0.03106 7 1

25 0.03106 7 2

26 0.03117 8 1

27 0.03117 8 2

28 0.05263 NONE

The CBC/HB estimation with dummy-data (300 respondents; 20000 total iterations) looks very weird. The graphs are all around 0 mean beta, Pct. Cert. is 0.214 and RLH is 0.336.

Question 3: But this is due to the uniform distributed dummy data or is there an error here that can also happen to me with real data?

Thank You in Advance