You estimated two alternative-specific constants, but you also define generic alternatives. By estimating two constants you essentially correct for left-to-right bias, but after correcting for that both constants need to be the same, so I would choose app_asc = 1.1. I do find the standard error of 0.14 suspiciously low. Further, using these constants still results in high probabilities for the optout alternatives. Do respondents in your pilot survey indeed select the optout in about 70% of the time? If not, please check the coding of the levels in your data and check your estimations, I cannot really help there.
With this many Bayesian priors, it is no longer computationally feasible to do 3 draws for any abscissa, so I think the best you can do is to use 2 draws for each coefficient.
I added ;con because I think you may want to optimise also for estimating the constant.
I reverted back to the modified Federov algorithm because now it is difficult to find designs without any dominant alternative. I set the candidate set to 2000, but this could lead to a very long computation time (you may need to run it for days), so you could decide to set it to 1000.
You would need to run this syntax for a long time (at least a day on a fast computer).
- Code: Select all
Design
;alts = app1*, app2*, neither
;rows = 48
;block = 4
;eff = (mnl,d,mean)
;alg = mfederov(candidates = 2000)
;con
;bdraws = gauss(2)
;model:
U(app1) = app_asc[(n,1.1,0.14)]
+ cost[(n,-0.446,0.03)] * COST[0,2.99,5.99,8.99](10-14,10-14,10-14,10-14)
+ developer.dummy[(n,0.172,0.229)|(n,-0.544,0.228)] * DEVELOPER[1,2,0]
? 0 = NHS digital; 1 Mhealth Essentials =; 2 = does not show;
+ ratings.dummy[(n,-0.948,0.27)|(n,-0.213,0.249)|(n,-0.638,0.256)] * RATINGS[1,2,3,0]
? 0 = 4.8 stars, 1 = 3.2 stars, 2 = 4 stars, 3 = does not show
+ description.dummy[(n,-0.081,0.244)|(n,-0.538,0.244)] * DESCRIPTION[1,2,0]
? 0 = short, 1 = long, 2 = brief
+ images.dummy[(n,-0.436,0.232)|(n,-0.107,0.224)] * IMAGES[1,2,0]
? 0 = logo and screenshot, 1 = screenshot, 2 = logo
/
U(app2) = app_asc
+ cost * COST
+ developer * DEVELOPER
+ ratings * RATINGS
+ description * DESCRIPTION
+ images * IMAGES
$
Michiel