Abstract:
Statements like “quality of care is more highly valued than waiting time” can neither be supported nor
refuted by comparisons of utility parameters from a traditional discrete choice experiment (DCE). Best–worst
scaling can overcome this problem because it asks respondents to perform a different choice task. However,
whilst the nature of the best–worst task is generally understood, there are a number of issues relating to the
design and analysis of a best–worst choice experiment that require further exposition. This paper illustrates
how to aggregate and analyse such data and using a quality of life pilot study demonstrates how richer
insights can be drawn by the use of best–worst tasks.
