I'm fairly new to the software and to the choice experiment area in general, so my question might seem obvious to those who know more than me. So, I apologize in advance for my naivety.

Anyway, I have (tried) to design a quite straightforward orthogonal 2x2x2x4 choice experiment, with two unlabelled alternatives. Besides main effects, I am interested in all two-way interactions between attributes, which led me to implement the foldover-option.

Here is my experimental design:

- Code: Select all
`Design`

;alts = SokandeA, SokandeB ;rows = 8 ;orth = seq ;foldover ;model:

U(SokandeA) = b0

+ b1 * KON[1,0]

+ b2 * ALDER[1,0]

+ b3 * LEDIG[1,2,3,0]

+ b4 * ENG[1,0]

/

U(SokandeB) = b1 * KON + b2 * ALDER + b3 * LEDIG + b4 * ENG

$

My question relates to the interaction effects. After collecting some data, I tried to estimate the choice model (using Stata and the clogit command). I then found out that I cannot estimate all interaction effects. After returning to experimental design, I realized that this must (?) be a consequence of the design itself, rather than the statistical model or the number of observations.

Here is the experimental design I used for my experiment, as produced by the software (sorry for the formatting):

Choice situation sokandea.kon sokandea.alder sokandea.ledig sokandea.eng sokandeb.kon sokandeb.alder sokandeb.ledig sokandeb.eng Foldover block

1 1 1 1 1 1 1 0 0 1

2 0 1 3 1 0 0 1 0 1

3 1 0 2 1 1 0 3 0 1

4 0 0 0 1 1 0 2 0 1

5 1 1 0 0 0 0 0 1 1

6 0 1 2 0 0 1 3 0 1

7 1 0 3 0 0 1 2 1 1

8 0 0 1 0 1 1 0 1 1

9 0 0 0 0 0 0 1 1 2

10 1 0 2 0 0 1 3 1 2

11 0 1 3 0 1 1 1 0 2

12 1 1 1 0 1 0 3 1 2

13 0 0 1 1 1 0 2 1 2

14 1 0 3 1 1 1 1 1 2

15 0 1 2 1 0 1 2 0 2

16 1 1 0 1 0 0 0 0 2

For me, it is clear that, it is indeed impossible to estimate the interaction ALDER*LEDIG and the interaction KON*LEDIG at the same time; for cases in which attribute LEDIG=3, attributes KON and ALDER are always mirror images of each other (i.e. when KON=1, ALDER =0, and vice versa). Therefore their effects cannot be distinguished from each other, conditional on the specific value of the attribute LEDIG. At least not in the regression context I have in mind.

So, my question is: What am I missing? Why doesn’t the foldover work in the way I (naively) thought it would?

Kind regards,

Peter