Labeled vs unlabeled
Hi Ngene community,
I am dealing with an experimental design related with two very different categories of cattle products (feed-iberian ham vs acorn-iberian ham). Consumers are used to choosing one of the two alternatives just due to the noticeable gap in quality and of course the corresponding price gap which roughly is the double for the acorn-iberian vs feed-iberian. In this regard, as you can see in the syntax below the experimental design was conceived to be labeled. Besides the price, three attributes are going to be tested for both alternatives with the particularity that the levels are exactly the same for both feed and acorn-hams. Nonetheless, as we think that the attributes sensitivities can be related with the alternative specific, we have set up different priors for both alternatives. Regarding, the price vectors as they are very different (the price vectors are just coding levels so unlike the rest of the attributes, they are representing very different levels of price despite using the same coding - 0,1,2,3 -) for both alternatives they are set up accordingly.
design
;alts = Feed, Acorn, Out
;rows = 24
;con
;block = 4
;bdraws = gauss(3)
;eff = (mnl,d,mean)
;store = all
;model:
U(Feed) = b1[0.2] ? (0.2)
+b2.dummy[(u,0.05,0.1)]*X2[1,0] ? (0.075)
+b3 [(u,0.05,0.15)]*X3[0,1,2,3] ? (0.15)
+b4.dummy[(u,0.05,0.25)|(u,0.05,0.10)]*X4[2,1,0] ? (0.15|0.075)
+b5 [(u,-0.25,-0.10)]*X5[0,1,2,3] ? (-0.2625)
/
U(Acorn) = b6[0.05] ? (0.05)
+b7.dummy[(u,0.05,0.20)]*X2[1,0] ? (0.125)
+b8 [(u,0.1,0.25)]*X3[0,1,2,3] ? (0.2625)
+b9.dummy[(u,0.1,0.40)|(u,0.05,0.15)]*X4[2,1,0] ? (0.25|0.1)
+b10 [(u,-0.50,-0.20)]*X10[0,1,2,3]$ ? (-0.525)
So far, I guess that everything is fine taking into account the uncertainty about the priors (we have no clue about them but our own forecast about the relative importance of one attribute over another). The thing is that all the attribute levels are the same for both alternatives but the price, so I was wondering if it would be possible to convert the design into an unlabeled version. To do that, we would create and additional attribute which represents both types of hams in substitution of the ASCs. The issue is that as we noted above the price vector of both hams is totally determined by the type of ham. So, below I provide a tentative version of the unlabeled version just as a framework for sharing my doubts.
Design
;alts=A*, B*, OPT-OUT
;rows=24
;eff = (mnl,d,mean)
;bdraws=gauss(3)
;bseed = 12345
;block=4
;store=all
;model:
U(A) = +b1.dummy[(u,0.05,0.15)] *X1[1,0] ? (0.1)
+b2 [(u,0.05,0.15)] *X2[0,1,2,3] ? (0.15)
+b3.dummy[(u,0.05,0.25)|(u,0.05,0.10)]*X3[2,1,0] ? (0.15|0.075)
+b4.dummy[(u,0.05,0.35)] *X4[1,0] ? (0.2) Type of ham
+b5 [(u,-0.60,-0.10)] *X5[0,1,2,3] ? (-0.450) Price
/
U(B) = b1*X1
+b2*X2
+b3*X3
+b4*X4
+b5*X5
/
U(OPT-OUT) = asc[(u,0,0.15)]$
These are my questions:
1). Does it make sense to go for an unlabeled design when the price vectors are alternative specific? If so, how could I do it? In this regard when the X4 attribute in the unlabeled design takes the value 1 the price vector would be: [(u,-0.50,-0.20)] and when it takes the value 0 the price vector would be: [(u,-0.25,-0.10)]. I guess that it is not possible to estimate specific price vectors nested according to the levels of the X4 attribute (acorn-iberian ham vs feed-iberian ham) but I would like to know your opinion.
2) As an alternative to this nested approach of the unlabeled design, I thought in just using the same price vector for both hams with more levels and uncertainty and them to recover the price sensitivity for both qualities making an interaction between b4*b5 (type of ham*price). While this could be feasible econometrically from my point of view the issue would be that the alternatives would be very unrealistic for the consumers since in the real market both types of ham do not display any kind of overlapping region in prices (acorn-iberian ham price is always noticeable higher than feed-iberian ham).
3) For the rest of the attributes to recreate that the consumer attribute sensitivity could be different for both types of ham, I would do the same interacting b4*b1, b2 and b3. In this case, as the attribute levels are the same there is no risk at all of having unrealistic choice tasks. If the interaction was not significant it would mean that there is no "type of ham effect" on attribute sensitivity.
Best wishes
Thank you in advance Professor Bliemer for your willingness to help!