There is an extensive and growing literature on the design and use of stated choice experiments. Such experiments are generally regarded as the preferred framework within which to collect data to reveal the preferences of individuals in a particular application context. Analysts have developed sophisticated ways of analysing such data, typically using a form of discrete choice model that identifies the marginal (dis)utility associated with each observed attribute linked to an alternative, as well as accounting for sources of preference and scale heterogeneity. There is also a growing literature studying the attribute processing rules (or heuristics) that respondents use as a way of simplifying the task of choosing, for all manner of meaningful reason. We find that there is relatively less effort placed on looking closely at the data defining each choice situation for each respondent, as constructed by the stated choice experiment, and seeing if there exist evidential. rules that support in a plausible way, the choice responses. Heuristics investigated in this paper that might aid in our understanding of how choice scenarios are processed, leading to a choice outcome, include the role of dimensional vs. holistic attribute processing, the influence of relative attribute levels, and the revision of the reference alternative as value learning across sequenced choice sets. We find a high level of confidence in the evidence, and identify at least two features of choice set processing, namely value learning and majority of confirming dimensions, that are worthy of future inclusion in the estimation of all choice models. The evidence suggests that there is a great deal of behavioural sense in stated choice responses, for all manner of possible reason.
choice experiments plausible choice heuristics sequence effects referencing reference revision value learning attribute processing majority of confirming dimensions evidential rules