The area of discrete choice modelling has seen a hype of activity over recent years, with the development of ever more flexible model structures that allow for a representation of complex error structures, in terms of substitution patterns between alternatives as well as random taste heterogeneity across individuals. However, while these new model structures represent mathematical advances, they do not per se lead to a deeper understandingof the actual choice processes undertaken by decision makers. In fact, itcan be argued that by allowing for a more flexible error structure, we simply make up for our relative lack of knowledge as to the true nature of choice behaviour represented in the data at hand. This is particularly truein the context of how and if individuals respond to changes in individualattributes. As such, not only do the sensitivities to such changes potentially vary across respondents, but it is entirely reasonable to expect that some individuals do not respond at all to certain attributes, or only evaluate certain attributes in conjunction with others. Additionally, some individuals may ignore certain attributes as long as their value remains below a certain threshold. Finally, there may be individuals who ignore certain attributes in situations where some other, higher-priority attribute attains or exceeds a certain threshold value. While variations in sensitivities are now routinely dealt with through the use of mixture models, the possibility of some individuals not responding to certain attributes (underany or certain conditions) is not generally dealt with in existing studies. This paper aims to give an in depth discussion of the potential effectsof ignoring respondents' attribute processing strategies, using a number of case studies to support the theoretical claims. The effects of ignoringrespondents' attribute processing strategies in model estimation are potentially very significant. The most basic scenario is one in which the population is split into two groups, of which one part is indifferent to changes in a certain attribute (either always or below a certain threshold value), where there is potentially additional variation in the sensitivity in the other (non-zero sensitivity) group. Depending on the weight of the zero-sensitivity group, the populationlevel estimates will be affected significantly by the presence of this group, in terms of parameter estimates as well as standard errors. While in the most basic models, this will lead toproblems in the fixed-point estimates, additional issues arise in the case of mixture models, where the estimated range will be affected in addition to the mean value. As such, the presence of a large number of respondents with a zerosensitivity to a certain attribute could lead to a significant probability of a positive coefficient even in the case where the coefficient is negative for the remainder of the population. This is especially likely when additionally making use of inflexible distributions such as theNormal. Similar issues clearly arise in the case where some individuals treat attributes A and B separately while the remainder of the population reacts only to changes in the combined value of A and B.t of this paper presents a number of case studies making use of stated preference (SP) data collected by the Institute of Transport and Logistics Studies at the University of Sydney. In this dataset, car drivers were given a choice between two routes, described by five attributes, namely free flow and slowed down travel time, travel time variability, running costs and toll costs. In addition to making a choice between the different alternatives (variably including and excluding the reference trip), respondents were asked to state if they had systematically excluded any attributes in the evaluation of thealternatives, and were also asked to rank the five attributes in order ofimportance. For the covering abstract see ITRD E135582.
Samenvatting