Dear Sukunta,

I notice that some of your bayesian priors have a very large standard deviation. I assume that these come from a pilot study and that you have used (n,parameter,standarderror) as Bayesian priors? Please check.

With such large standard deviations, it is quite likely that some of your draws will lead to quite extreme values the coefficients, which results in issues in generating a design because some attributes will become dominant and choice probabilties will be pushed towards 0 and 1.

One way to limit the issue is to use the median Bayesian D-error instead of the mean Bayesian D-error. Medians cannot be computed using Gaussian draws, so in this case I suggest switching to Sobol draws.

Another issue is the number of rows used. While with 6 rows you can estimate a choice model, it is really the bare minimum and there will not be much variation in your data. I suggest using 12 or 24 rows, blocking the design in 2 or 4 (such that each respondent still faces 6 choice tasks, you simply create 2 or 4 different versions of the survey questions).

For example, see the syntax below.

- Code: Select all
`design`

;alts=alt1*,alt2*,alt3

;rows=12

;block=2

;eff = (mnl,d,median)

;con

;bdraws = sobol(5000)

;model:

U(alt1)=b0 [(n,5.51892, 2.400485)]+b1.dummy[(n,-.1969752, 0.6256593) |(n, -1.777203, 1.348568)]*mode[1,2,0]+b2.dummy[(n, -0.0287256, 0.20309630)]*format[1,0]+b3.dummy[(n,1.368347, 1.700064)]*con_ser[1,0]+b4[(n,-.0268496, .0316639)]*time[10,20,30]+ b5 [(n,-.0297015, .0246112)]*cost[0,50,100]/

U(alt2)= b0 [(n,5.51892, 2.400485)]+b1*mode+b2*format+b3*con_ser+b4*time+b5*cost

$

I would run the syntax for at least an hour (there is no harm in running it overnight). Once the Bayesian D-error has stabilised (i.e. doesn't decrease much anymore), you can stop and pick the design with the lowest Bayesian D-error. I often also look at the Sp estimates, which give you an idea about the required sample size needed for statistical significance (at 95% confidence level) for each parameter (assuming that your priors reflect the true parameter values). Note that Sp-estimates assume that a respondent faces all questions in the design, so if you have rows = 12 and block = 2, then you need to multiply the Sp-estimates by 2 to get an indication for the required number of respondents.

Michiel