Task complexity, framing effects and post-hoc individual-level model analysis in discrete choice experiments.

This project deals with three important issues in discrete choice experiments (DCEs) which are widely used to study preferences for attributes of competing products or services in various areas of economics. To maximize the information content of the data from DCEs, it is crucial to design the experiments optimally. In our search so far, we have focused on improving the statistical quality of DCEs. However, the statistical quality is not the only aspect to consider. The response quality of a DCE is at least as important and depends on whether respondents can answer the choice questions well, that is, whether the choice questions are not too complex. Also, the framing or the labelling of the attributes and attribute levels plays a key role. Positive frames generally stimulate risk-averse responding as opposed to negative frames. Accounting for each of these two difficulties in the design and analysis of DCEs each makes up a part of this project. The designs we aim to construct will score well on overall quality, which includes both statistical quality and response quality. A final part of the project is devoted to post-hoc individual-level discrete choice modelling in which we show how to use individual preferences for market segmentation and the construction of indifference maps.

Keywords:ECONOMICS

Disciplines:Applied economics, Economic history, Macroeconomics and monetary economics, Microeconomics, Tourism