I’m using NGENE to develop an efficient design aimed at estimating the brand equity of various consumer goods brands, employing a mixed logit model in WTP space. I have two questions regarding the potential impact of brand-specific priors on WTP estimates.
First, I’d like to understand if the mean WTP estimates are sensitive to these priors. For example, if a brand has a lower prior (e.g., a lower initial WTP level), it might appear with lower prices in the choice sets. Would this increase its selection frequency, potentially leading to an overestimation of its WTP? Or is this interpretation flawed?
Second, I’m curious if the standard deviation of the WTP distribution might also be influenced by design priors. For example, Heineken—a strong brand in the beer category—exhibits a high WTP and a high standard deviation. If the efficient design frequently presents Heineken at higher price levels (relative to other brands due to its high prior), could this lead more price-sensitive respondents to ascribe lower preference to it, while those less sensitive remain willing to pay the higher prices? Does this reasoning make sense?
Finally, if these issues are possible, might an orthogonal design—lacking dependency on priors—help mitigate them?
Thank you in advance for any insights or experiences you can share.
My code follows below. Heinken is the brand = 5.
- Code: Select all
Design
;alts = Alt1*, Alt2*, Alt3*, Optout
;rows = 80
;block = 10
;eff = (mnl,d,mean)
;bdraws = mlhs(500)
;alg = mfederov(candidates = 5000, stop=total(20000 iterations))
;require:
Alt1.brand <> Alt2.brand,
Alt1.brand <> Alt3.brand,
Alt2.brand <> Alt3.brand
;model:
U(Alt1)= b_brand.dummy[(u,0.3,0.6)|(u,0.0,0.3)|(u,0.3,0.6)|(u,0.1,0.4)|(u,0.3,0.6)|(u,0.1,0.4)|(u,0.1,0.4)] * brand[1, 2, 3, 4, 5, 6, 7, 8] +
b_price.dummy[(u,-0.2,-0.1)|(u,-0.3,-0.2)|(u,-0.4,-0.3)|(u,-0.5,-0.4)|(u,-0.6,-0.5)|(u,-0.7,-0.6)|(u,-0.8,-0.7)|(u,-0.9,-0.8)] * price[3.9,4.3,4.8,5.3,5.9,6.5,7.2,8.0,3.5] +
b_taste.dummy[(u,0,0.3)] * taste[2,1] /
U(Alt2)= b_brand.dummy*brand + b_price*price + b_taste.dummy*taste /
U(Alt3)= b_brand.dummy*brand + b_price*price + b_taste.dummy*taste $