The implications of respondent information processing rules on preference revelation in stated choice experiments

Abstract

Individual’s process the information in stated choice (SC) experiments in many different ways. In order to accommodate decisions rules that are used in processing information, there is good sense in conditioning the parameterisation of stated choice design attributes on these rules. In particular, rules might be invoked to cope with the dimensionality of the SC design. The information processing strategy (IPS) can act as a qualifier of heterogeneity in the mean and/or heteroscedasticity of the distribution of the marginal (dis)utility of design attributes. In this paper we investigate the impact of rules such as attribute aggregation and reference dependency on the mean and the standard deviation parameters to produce revised preference profiles for specific design attributes, as we vary the dimensionality of an SC design. By contrasting mixed logit models with and without IPS conditioning, we are able to establish the extent of parameter shift and the implications on outputs such as willingness to pay. The empirical evidence, drawn from a study in Sydney of car commuter route choice, suggests that accounting for the way that stated choice designs are processed, given their dimensionality, does make a statistically significant difference on the profile of preferences for specific attributes and alternatives. This evidence has value in guiding the design of SC experiments and in adjusting results from different SC designs when comparing the evidence.