Hi Ngene team,
Thanks for your great support and helpful manual and articles. I am trying to create an efficient design to evaluate WTP for electric vehicles in Canada. Before doing the main SP survey, we will do a pretest where we can get some ideas about parameter priors. I am thinking of doing a pivot design in which I would have three alternatives (status quo, hybrid vehicle, and pure electric vehicle). I will have six vehicle attributes, all having four levels. Regarding such experimental design on Ngene, here are my questions (please correct me if I’m wrong or missed any points):
1) Do I need to include alternative specific constant in the utility equation syntax?
2) Is that OK if some of the attributes are pivoted and some not ( e.g. attribute X1 -on the syntax below in question 6- is pivoted while X2 is not )
3) I also need to include sociodemographic characteristics in alternative utilities. Do I need to know and include all demographics in advance? (There might be more than eight of them that I want to estimate)
4) For the pretest, I know the signs of the parameters from the previous literature and I am thinking of doing Bayesian design with three segments (similar to the example in manual p.166). For the segments though, I’m not very clear about the fixed parameter in [ ] for reference alternative -- I wonder if the fixed parameter I choose for each attribute should be relevant to the one of another attributes in each segment or they are independent from one another?
for instance, if the fixed parameter for vehicle price is specified a small number, the parameter I consider for other attributes, let’s say pollution level, should be relevant to the specified price?
5) Is that OK if I optimize my design with MNL model and at the end, estimate my model with MMNL model with actual data?
6) Here is my syntax (I didn't consider the three segments this time):
Design
;alts = alt1, alt2, alt3
;rows = 48
;block = 6
;eff = (mnl,d)
;model:
U(alt1) = b1[(u,-1,0)] * X1.ref[20] + b3[(u,-0.5,0)] * X3.ref[12] + b4[(u,-0.3,0)] * X4.ref[8] + b6[(u,-0.05,0)] * X6.ref[4] /
U(alt2) = b1 * X1.piv[-25%,0%,25%,50%] + b2 * X2[0,1,2,3] + b3 * X3.piv[-80%,-60%,-40%,-20%] +
b4 * X4.piv[-15%,-5%,5%,15%] + b5 * X5[0,1,2,3] + b6 * X6.piv[-90%,-70%,-50%,-30%] +
b7 * X7[0,1,2,3] /
U(alt3) = b1 * X1.piv[-25%,0%,25%,50%] + b2 * X2[0,1,2,3] + b3 * X3.piv[-80%,-60%,-40%,-20%] +
b4 * X4.piv[-15%,-5%,5%,15%] + b5 * X5[0,1,2,3] + b6 * X6.piv[-90%,-70%,-50%,-30%] +
b8 * X8[0,1,2,3] $
I ran the above syntax and scanned all 48 scenarios I got. I noticed that Ngene keeps giving me alternatives with the same level for many scenarios. I even tried a very simple design with far less number of attributes and levels, but still got the same issue -- below is an example of one of such scenarios, you can see that x1, x3, x4 and x2 all have the same level for alt2 and alt3:
alt1 alt2 alt3
x1 20 30 30
x3 12 5 5
x4 8 8 8
x6 4 1 2
x2 2 2
x5 1 2
x7 1
x8 1
Choice question:
Many thanks and I already apologize for my lengthy questions!