Hi!
I am totally new, have read through all relevant posts and most parts of the manual, but still struggles, and have really tight time limit.
We are running a DCE on 1200 respondents in Tanzania on preferences about president candidates and gas revenue distribution (3 regions 400, each), as part of a bigger survey.
We are piloting on thursday, and have then 1 day to make changes => if we want more efficient design.
For Pilot we used these attributes:
gender (man, woman)
party (ruling, oposition)
corruption (never, accused)
education (free, not free)
reg_nat (equal share, gas region benefit)
expectations (exaggerate, moderate)
earnings increase (as result of president coming to power)
Design
;alts = President_A, President_B
;rows = 20
;orth = seq
;eff = (mnl, d)
;block = 2, minsum
;model:
U(President_A) =
b1 * gender [0,1] +
b2 * party [0,1] +
b3 * corruption [0,1] +
b4 * education [0,1] +
b5 * reg_nat [0,1] +
b6 * expectations [0,1] +
b7 * earnings_increase [1000,5000,10000,15000] /
U(President_B) =
b1 * gender +
b2 * party +
b3 * corruption +
b4 * education +
b5 * reg_nat +
b6 * expectations +
b7 * earnings_increase $
QUESTIONS
1. Based on sample size (1200), nr of choice sets (10*2), attributes (7). and no priors, should I use:
orth = seq, eff = (mnl,d) => d-error = 0.02
OR
orth = ood => gives d-Optimality = 95%
OR
something else?
2a. If we receive priors (OR make some guesses). How much better do you think our choice sets will be? What improves? Smaller st.dev?
2b. Should we then remove ;orth=seq, but keep ;eff=(mnl, d) ? Or should we add some more "stuff"?
Really sorry about basic questions, but we have used Orthogonality because we dont understand the others, would love to get these questions answered .
Thanks!