Dear fellow researchers

Currently, I am designing a DCE pilot study in Ngene.

I plan to use Bayesian efficient experimental design for the main survey, optimize for mean D-efficiency of the MNL model. The design will include 24 choice tasks per respondent and 6 blocks of choice cards (24 cards in total). Each respondent will receive 4 choice card. The utility function will be assumed that all attributes were dummy-coded, except for the cost, which was continuous. The priors for the efficient design will be based on the pilot study and generic across the three cities.

However, since I haven’t found similar studies, the pilot study can not use priors based on the result s of the earlier studies.

Therefore, I was designing a DCE pilot study in Ngene by using the D-error measure for finding an efficient design for the MNL model, I opted to include near - zero priors for all attributes and not a mixture of near-zero and literature priors.

Now. I have an unlabeled experiment experiment, with 2 alternatives + status quo (current situation).

- Three alternatives (two plus one status quo(sq))

- 4 attribute, with 4, 3, 4, 6 levels respectively.

- the sq is defined by the same attributes but the levels are fixed and gives a fixed utility

For two of the attributes sorting[1,2,4,7] (dummy) and collected[1,2,3] (dummy), I choose the lowest level 1 to be the level of the status que and the level 1 is also a level for alt1 and alt2.

For two other attributes point[0,1,2,3] (dummy) and cost[0,20,40,70,100,200], I choose the lowest level 0 to be the level of the status que, but the level for the status que only appears in the status que and not in the other two alternatives.

Below is my codes

Design

;alts = alt1*,alt2*,sq*

;rows = 24

;block=6,minsum

;eff = 2*(mnl, d) + 1*(imbalance)

;alg = mfederov(candidates=1000)

;require:

alt1.cost > 0, alt2.cost > 0, alt1.point > 0, alt2.point > 0,

sq.sorting = 1,

sq.collected = 1,

sq.point = 0,

sq.cost = 0

;model:

U(alt1) = b1.dummy[0.001|0.002|0.003] * sorting[1,2,4,7]

+ b2.dummy[0.001|0.002] * collected[1,2,3]

+ b3.dummy[0.001|0.003|0,002] * point[0,1,2,3]

+ b4[-.001] * cost[0,20,40,70,100,200]

/

U(alt2) = b1*sorting+b2*collected+b3*point+b4*cost

/

U(sq) =b1*sorting+b2*collected+b3*point+b4*cost

$

Could I ask some questions about my design?

First, do you think the parameter I choose is appropriate for the pilot study?

Second, the B-estimate is 73%, quite low, does there have some way to fix this?

Third, since I give same utility function for attribute sq, do I need to add constant?

Fourth, since I want each respondent who face 4 choice cards, do you think 24 rows and 6blocks good for the design?

Thank you for your help.

Best

Steve