I get a much larger D-error than 0.85 when I run that syntax. This is due to the fact that your number of rows is too small, you need more variation to efficiently estimate the 9 parameters in your model. I propose that you use 12 rows and block the design in 2 versions such that each respondent still only faces 6 choice tasks. You can also increase the number of choice tasks you give to a respondent to increase efficiency. If you only have 140 respondents, you may actually consider giving 12 choice tasks to a single respondent.
Further note that your Bayesian priors have a high degree of unreliability as indicated by their standard deviations, therefore sample size estimates will also have a high degree of uncertainty.
- Code: Select all
design
;alts=alt1*,alt2*,alt3
;rows=12
;block=2
;eff =(mnl,d,mean)
;con
;bdraws = gauss(3)
;model:
U(alt1) = b0[(n,2.334779,0.555722)]
+ b1.dummy[(n,-1.0134, 0.353174)|(n,-0.88372,0.43772)] * visit[1,2,0]
+ b2.dummy[(n,-0.82938, 0.4533)] * hap[1,0]
+ b3.dummy[(n, 0.147476, 0.495515)|(n,-0.8025,0.543363)] * plan[1,2,0]
+ b4.dummy[(n,0.693785,0.26066)] * excer [1,0]
+ b5.dummy[(n,0.139101,0.501286)] * behav[1,0]
+ b6.dummy[(n, -0.37844, 0.584715)] * menu[1,0]
/
U(alt2) = b0
+ b1 * visit
+ b2 * hap
+ b3 * plan
+ b4 * excer
+ b5 * behav
+ b6 * menu
$
This syntax generates a design with a Bayesian D-error of 0.82 (fixed D-error of 0.66). You can estimate all parameters, but some may not be statistically significant. If your prior value are correct, the first dummy coded parameter of b3 and b5 may not be statistically significant with 140 respondents, but all other ones would be.
Michiel
