Hello stat talkers, :wave:
I'm currently working on a marketing (discrete choice) project concerning brand choice and I stumbled upon some questions I would like to ask here.
in Louviere et al "stated choice methods" page 63 the authors write: "...any variable that is not an attribute of an alternative of the choice set (e.g. not attribute of brand A, B, or C ; such as the individuals gender), cannot be included as a seperate variable in all utility expressions, since it does not vary across the alternatives(brands). ... To enable a non-brand attribute to be included in all utility expressions, it must be interacted with an alternative-specific attribute."
Question A: What is the consequence of these variables not variing across alternatives in terms of estimation of parameters for the model? Does somebody have an idea where to read more on specifically this matter?
Question B: Does this mean that when I am interested in if women are more likely to choose brand A, B, or C, I cannot include gender in all utility-specifications, yet if I include the gender price interaction (to see how much women pay more for a certain brand) than I can include my gender AND the gender*price interaction? (I do have to include the main effects when using interactions in a regression context)
Another interpretation of what the authors say is that I should only include the interaction (instead of the two variables: one brand-specific, one individual-specific) but only do so if the created variable is interpretable (e.g. price / income could be relative cost).
I would be happy if some of you would share your thoughts of this with me.
from Germany
alex
I'm currently working on a marketing (discrete choice) project concerning brand choice and I stumbled upon some questions I would like to ask here.
in Louviere et al "stated choice methods" page 63 the authors write: "...any variable that is not an attribute of an alternative of the choice set (e.g. not attribute of brand A, B, or C ; such as the individuals gender), cannot be included as a seperate variable in all utility expressions, since it does not vary across the alternatives(brands). ... To enable a non-brand attribute to be included in all utility expressions, it must be interacted with an alternative-specific attribute."
Question A: What is the consequence of these variables not variing across alternatives in terms of estimation of parameters for the model? Does somebody have an idea where to read more on specifically this matter?
Question B: Does this mean that when I am interested in if women are more likely to choose brand A, B, or C, I cannot include gender in all utility-specifications, yet if I include the gender price interaction (to see how much women pay more for a certain brand) than I can include my gender AND the gender*price interaction? (I do have to include the main effects when using interactions in a regression context)
Another interpretation of what the authors say is that I should only include the interaction (instead of the two variables: one brand-specific, one individual-specific) but only do so if the created variable is interpretable (e.g. price / income could be relative cost).
I would be happy if some of you would share your thoughts of this with me.
from Germany
alex