Bayesian design

This forum is for posts that specifically focus on Ngene.

Moderators: Andrew Collins, Michiel Bliemer, johnr

Bayesian design

Postby eliD » Wed Oct 03, 2018 8:55 pm

Dear Ngene team,

I am currently working on a new consumer survey that will include a CE. I would like to use a Bayesian design.
As a first step, following the indications in the manual and those that I found in this forum I run a d-efficient design based on a mnl model, setting all priors equal to zero. This is because I cannot really guess about the direction of the attribute levels, except for price which is expected to be negative. Using the sintax below I obtained a d-error equal to 0.345, which I suppose (please correct me if I am wrong!) may be good enough for a pilot survey.

Design
;alts = alt1, alt2, alt3
;rows = 28
;block = 4
;eff = (mnl,d)
;model:
U(alt1)= b1[0]*price[0.33, 0.39, 0.45, 0.51, 0.57, 0.63, 0.69] + b2.dummy[0|0|0]*Plastic[0,1,2,3] + b3[0]*Colour[0,1]/
U(alt2)= b1*Price + b2.dummy*Plastic + b3*Colour
$

However, I was wondering whether, in this phase, it would be better to use uninformed Bayesian priors with uniform distribution e.g., [(u, 0, 1)] (assuming that attribute levels would have the same impact on the utility) instead of setting priors equal to zero.
Furthermore, since I cannot refer to the S-estimate how can I establish the proper number of respondents for the pilot test in order to obtain reliable priors for the final Bayesian design?
I apologize in advance if these issues have already been asked before.
Thank you very much for your help.
Eli
eliD
 
Posts: 4
Joined: Thu Mar 30, 2017 2:17 am

Re: Bayesian design

Postby Michiel Bliemer » Thu Oct 04, 2018 5:22 pm

Hi Eli,

The actual value of the D-error does not have much meaning, so it is not possible to say whether 0.345 is good or bad, all we know is the lower the better, but what is achievable depends entirely on the study (sometimes 0.1 is bad, in other cases 0.1 is very good). I think that your syntax looks fine.

Using priors equal to zero or using Bayesian priors around zero (or constrained with a sign as you indicate) are both fine. I generally have a preference for Bayesian priors since it makes the design more robust, but without any information from a pilot study it is sometimes a bit difficult to set the priors. If you know the design you could indeed use (u,0,1) and if you do not know the sign you could use for example (n,0,1).

Without informative priors you cannot determine an appropriate sample size for the pilot study. Most people keep the sample size for a pilot study relatively small. It is not necessary that all parameters can be estimated with a high level of significance based on the pilot study, you simply insert them as Bayesian priors with uncertainty. So if the parameter estimate for b1 is 0.5 and its standard error is 0.7 (i.e., it is not statistically significant), then you use as Bayesian prior (n,0.5,0.7) when generating a design for your main study.

Michiel
Michiel Bliemer
 
Posts: 568
Joined: Tue Mar 31, 2009 4:13 pm

Re: Bayesian design

Postby eliD » Thu Oct 04, 2018 6:16 pm

Michiel,

thank you very much for your kind and prompt reply. Your suggestions really help!

Thanks again,
Eli
eliD
 
Posts: 4
Joined: Thu Mar 30, 2017 2:17 am

Re: Bayesian design

Postby eliD » Thu Oct 04, 2018 8:27 pm

Dear Michiel,

I'm sorry to disturb you again, but I have a further question.
I just ran the design, but checking the output I found some repetitions (same choice situation in block 1 and 3). So I added the star to alt1 and alt2 as follows:

Design
;alts = alt1*, alt2*, alt3
;rows = 28
;block = 4
;eff = (mnl,d)
;model:
U(alt1)= b1[0]*price[0.33, 0.39, 0.45, 0.51, 0.57, 0.63, 0.69] + b2.dummy[0|0|0]*Plastic[0,1,2,3] + b3[0]*Colour[0,1]/
U(alt2)= b1*Price + b2.dummy*Plastic + b3*Colour
$

I obtained a design without repetitions (d-error is unchanged), but I am not sure that using the * command is appropriate in this case. If I have understood well reading some posts in this forum, it is not recommended to use the * command when using priors equal to 0 and when the signs are unknown. Any feedback will be much appreciated.

Thank you in advance for your help.

Eli
eliD
 
Posts: 4
Joined: Thu Mar 30, 2017 2:17 am

Re: Bayesian design

Postby Michiel Bliemer » Fri Oct 05, 2018 9:10 am

Adding the * is a good idea to avoid repetitions. Since you are using zero priors, Ngene cannot check for dominance but that is fine.

I only now notice that you have a no-choice alternative (alt3). Note that you need add either a constant for alt1 and alt2 (the same constant, eg b0), or a constant in alt3.

Michiel
Michiel Bliemer
 
Posts: 568
Joined: Tue Mar 31, 2009 4:13 pm

Re: Bayesian design

Postby eliD » Fri Oct 05, 2018 6:33 pm

Thank you so much Michiel!
Eli
eliD
 
Posts: 4
Joined: Thu Mar 30, 2017 2:17 am


Return to Ngene

Who is online

Users browsing this forum: No registered users and 3 guests

cron