PURPOSE: The purpose of this workshop is to give participants an overview of preference elicitation instruments other than discrete choice experiments and to help them understand the opportunities and challenges related to drawing from a wider set of tools.

DESCRIPTION: Discrete choice experiments (DCEs) have become the predominant instrument for eliciting stated preference data. This has primarily two reasons: 1) DCEs and statistical methods used for analyzing their data have a firm theoretical foundation; 2) a large body of methodological research helps practitioners understand best-practice, strengths, and weaknesses. However, DCEs are not always best-suited for all research questions, due to their complexity, sample size requirements and design limitations. Alternative methods such as the threshold technique and swing weighting can be considered instead. These methods can overcome some limitations of DCEs by eliciting valid preferences at the individual level, even if the sample size is small and the decision context complex. However, all methods have their limitations that should be weighed against their strengths when considering whether they are appropriate for use in a given situation. During this workshop, discussion leaders will introduce different preference elicitation methods, illustrate that they produce valid estimates and help the audience understand the importance of selecting a fit-for-purpose preference elicitation method.

Brett Hauber will motivate the discussion of widening the set of preference elicitation methods that researchers regularly draw from. He will give an overview of the uses and challenges of different approaches and what to consider when identifying an approach that is fit for purpose. Jorien Veldwijk will compare the classical threshold technique and DCEs for assessing maximum acceptable risk. Karin Groothuis-Oudshoorn will compare swing-weighting with DCE for eliciting relative attribute importance. Sebastian Heidenreich will illustrate how to use multidimensional thresholding to elicit optimal trade-offs between multiple attributes in small-sample settings.