Comparing designs for choice experiments using various models: A case study

Abstract:

This talk describes an empirical comparison of the performance of four designs for a discrete choice experiment. These designs were chosen to represent the range of construction techniques that are currently popular for choice experiments when no prior knowledge of the parameters is available. Each design had 320 respondents who each completed 16 choice sets. The results suggest that for the multinomial logit model (MNL) the design that is used at this stage is fairly unimportant. As the sample size gets smaller, however, differences between the designs become apparent. We also analysed the results using four different models which accommodate preference heterogeneity. We find that any of these models are better predictors of observed choices than the MNL model for the designs used here, and that the differences across designs are larger for models with more parameters, although the gain appears to depend on the underlying preference structure. This work was partially supported by the ARC and was carried out with Leonie Burgess and Nada Wasi.

About the speaker: Deborah Street is Professor of Statistics in the Department of Mathematical Sciences and the Co-Director of the Centre for the Study of Choice (CenSoC) at the University of Technology Sydney. The general area of her research is in the construction of designed experiments. Her most recent work has been the construction of optimal and small (near-)optimal discrete choice experiments (DCEs), used extensively in marketing, transportation research, environmental economics and health economics.