- Elgar original reference
Edited by Stephane Hess and Andrew Daly
Best-Worst Scaling (BWS) can be a method of data collection, and/or a theory of how respondents provide top and bottom ranked items from a list. We begin with a brief history, followed by motivations for the use of BWS. The three types (‘cases’) of BWS will then be described in detail, before issues in the conceptualisation (modelling) and analysis of best-worst data are discussed. We present the simplest models of BWS, both for expository reasons, and because they have interesting (score) properties that have been found useful in data analyses. At various places, especially in section 5, we cite some more complex models that can handle intra-option dependencies, preference heterogeneity across decision makers, context effects, and so on. Busemeyer and Rieskamp (Chapter 3 of this volume) discuss parallel models for such effects in best choice; their is no principled reason why, as data warrants, those models for best choice can not be extended to best-worst choice.
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