Abstract
We examine the impact of individual-specific information processing strategies (IPSs) on the inclusion/exclusion of attributes on the parameter estimates and behavioural outputs of models of discrete choice. Current practice assumes that individuals employ a homogenous IPS with regards to how they process attributes of stated choice (SC) experiments. We show how information collected exogenous of the SC experiment on whether respondents either ignored or considered each attribute may be used in the estimation process, and how such information provides outputs that are IPS segment specific. We contend that accounting the inclusion/exclusion of attributes will result in behaviourally richer population parameter estimates.
Acknowledgement
The suggestions of three referees have materially improved this article.
Notes
Notes
1. This will particularly be the case if the constrained triangular or log-normal distributions are used. Whilst these distributions force the parameter estimates to be of the same sign, they also ensure that few, if any, individual-specific parameter estimates will be zero.
2. To demonstrate, consider the situation where attribute xjtq
is the price for alternative j in choice situation t. For all but giffen goods, setting the price to equal zero will likely make that alternative much more attractive relative to other alternatives in which the price is not equal to zero. Further, the procedure for maximising L*(θ) will be ignorant of the fact that setting xjtq
= 0 represents the exclusion of that attribute in the choice process and will estimate a value of
k
assuming that the value observed by the decision maker in choice situation t was zero for that attribute when indeed it was not. As such, setting xkjt
= 0 will not guarantee that the parameter for that attribute will be equal to zero for that choice situation. It is therefore
k
that should be set to zero in the estimation process, not xkjt
.