I am designing a simple between-subject experiment in which the respondents are split into a treatment/control group. My goal is to test whether the treatment affects the preference for the attributes/levels.
In the past, I tried to use the treatment covariates, but I was not successful (see this discussion http://choice-metrics.com/forum/viewtopic.php?f=2&t=404#p1487. This time, I am trying to use scenarios.
The CE is very simple, with only 2 attributes with 2 levels each.
To exemplify, I am using the full factorial design.
- Code: Select all
Design
;alts = alt1, alt2
;rows = all
;fact
;model:
U(alt1) = ASC1[-0.8]
+ b1.dummy[0.9] * A[1,0]
+ b2.dummy[1.0] * B[1,0]
/
U(alt2) = b1 * A
+ b2 * B
$
This code generates 16 choice tasks.
I am trying to include the experiment's treatment/control variable as a scenario (the T attribute), and I wrote this code:
- Code: Select all
Design
;alts = alt1, alt2
;rows = all
;fact
;model:
U(alt1) = ASC1[-0.8]
+ bT.dummy[0.5] * T[1,0]
+ b1.dummy[0.9] * A[1,0]
+ b2.dummy[1.0] * B[1,0]
+ iAT[0.6] * A * T.dummy[1]
+ iBT[0.7] * B * T.dummy[1]
/
U(alt2) = bT * T[T]
+ b1 * A
+ b2 * B
+ iAT * A * T
+ iBT * B * T
$
This generates 32 choice tasks, basically duplicating the previous one.
It seems to work and I get no errors, but I am afraid something is not right.
Specifically, the part of the code defining the treatment dummy bT.dummy[0.5] * T[1,0] should add to the ASC1, but it should not be included in the alternative 2. However, if I eliminate the code bT * T[T], the variable alt2.t appears anyway, and is not anymore the same as
alt1.t.
I guess a better function would exclude the treatment constant and include the interaction term between the ASC and the treatment. However, I do not know how to do it. Do you have any suggestion?