Dear All
Does anyone have experience in applying message frames in choice experiments?
I am carrying out a choice experiment with a d-efficient design. I will be evaluating the acceptance of different suboptimalities of food products. In each choice task the consumer will have the possibility to chose between a product as it can be found in the supermarket (alt:ganz) and the same product with a suboptimality (alt: kaputt).
Apart from that I want to figure out under which incentives it is more likely that the participants will accept a food product with a suboptimality. Which is why I will be applying message frames. I will divide my sample into three groups (two treatment and one control group). Before entering the the choice task session they will be randomly assigned to one group and read a corresponding message (no frame, gain frame and normative frame).
I am about to start my pre-test, but I am not sure if and how I should account for the different treatment groups when estimating the parameters. Ideally, in the end I would like to draw conclusions about which treatment group is more likely to accept what type of suboptimality of which product. Below you can find my design. I would be very grateful for you comments.
Thank you in advance
Best regards
Eli
;alts = ganz, kaputt
;rows = 36
;eff = (mnl,d)
;block=6
;cond:
if(kaputt.produkt = [1], kaputt.mangel <> 5),
if(kaputt.produkt = [3], kaputt.mangel <> 5),
if(kaputt.produkt = [4], kaputt.mangel <> 2),
if(kaputt.produkt = [4], kaputt.mangel <> 3),
if(kaputt.produkt = [5], kaputt.mangel <> 5),
if(kaputt.produkt = [6], kaputt.mangel <> 2),
if(kaputt.produkt = [6], kaputt.mangel <> 3),
if(kaputt.produkt = [6], kaputt.mangel <> 5),
if(kaputt.produkt = [7], kaputt.mangel <> 5),
if(kaputt.produkt = [8], kaputt.mangel <> 2),
if(kaputt.produkt = [8], kaputt.mangel <> 3),
if(kaputt.produkt = [8], kaputt.mangel <> 5),
if(kaputt.mhd > 1 ,kaputt.mangel = 1),
if(kaputt.mhd < 1 ,kaputt.mangel <> 1)
;model:
U(ganz) = 0
/
U(kaputt) = b_prod.d[0|0|0|0|0|0] * produkt[1,3,4,5,6,7,8] +
b_mangel.d[-0.01|-0.01|-0.01] * mangel[1,2,3,5] +
b_mhd[-0.0000001] * mhd[0,0.001,0.01,25,50] +
b_p[0.0000001] * preis[0,10,20,30,40]
$