Estimation of coefficients labeld choice experiment

This forum is for posts that specifically focus on the Windows desktop version of Ngene (i.e. all version 1.x releases).

Moderators: Andrew Collins, Michiel Bliemer, johnr

Post Reply
Michael_D
Posts: 5
Joined: Wed Nov 15, 2023 5:16 pm

Estimation of coefficients labeld choice experiment

Post by Michael_D »

Hello,

I have designed a labelled choice experiment with three alternatives. Lets say the alternatives are Apples, Ananas and Bananas. An attribute is price. Now the price varies between all choice sets in all alternatives.

For example choice set 1:
Apples 1 €
Ananas 1.5 €
Bananas 0.5 €

choice set 2:
Apples 1.5 €
Ananas 1.5 €
Bananas 1.0 €

Maybe my question is stupid but I asked myself whether I estimate a price coefficient for all three alternatives or only for two of them and one is the base?

Thank you for your help. Best regards
Michael
Michiel Bliemer
Posts: 2055
Joined: Tue Mar 31, 2009 4:13 pm

Re: Estimation of coefficients labeld choice experiment

Post by Michiel Bliemer »

One would often estimate a single price coefficient assuming price as a numerical attribute, whereby the prices for different fruits can be different:

U(apples) = ... + b_price * price_apples[0.5,1.0,1.5] + ... /
U(ananas) = ... + b_price * price_ananas[1.5,2.0,2.5] + .../
U(bananas) = ... + b_price * price_bananas[1.0,1.5,2.0] + ...


But it is possible to estimate alternative-specific coefficients, here you would estimate 3 coefficients:

U(apples) = ... + b_apple * price_apples[0.5,1.0,1.5] + ... /
U(ananas) = ... + b_ananas * price_ananas[1.5,2.0,2.5] + .../
U(bananas) = ... + b_bananas * price_bananas[1.0,1.5,2.0] + ...


You could also treat price as a categorical variable and estimate two coefficients for two dummy variables, assuming the last level is the base level. This requires that all price attributes have the same levels:

U(apples) = ... + b_price.dummy[0|0] * price[0.5,1.0,1.5] + ... /
U(ananas) = ... + b_price.dummy * price + .../
U(bananas) = ... + b_price.dummy * price + ...


Alternatively, you could consider alternative-specific dummies and estimate 6 coefficients:

U(apples) = ... + b_apple.dummy[0|0] * price_apples[0.5,1.0,1.5] + ... /
U(ananas) = ... + b_ananas.dummy[0|0] * price_ananas[1.5,2.0,2.5] + .../
U(bananas) = ... + b_bananas.dummy[0|0] * price_bananas[1.0,1.5,2.0] + ...


Michiel
Post Reply