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Best-worst scaling (BWS) is an extension of the method of paired comparison to multiple choices that asks participants to choose both the most and the least attractive options or features from a set of choices. It is an increasingly popular way for academics and practitioners in social science, business, and other disciplines to study and model choice. This book provides an authoritative and systematic treatment of best-worst scaling, introducing readers to the theory and methods for three broad classes of applications. It uses a variety of case studies to illustrate simple but reliable ways to design, implement, apply, and analyze choice data in specific contexts, and showcases the wide range of potential applications across many different disciplines. Best-worst scaling avoids many rating scale problems and will appeal to those wanting to measure subjective quantities with known measurement properties that can be easily interpreted and applied.
- Sales Rank: #519208 in Books
- Published on: 2015-09-23
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x .83" w x 6.85" l, .0 pounds
- Binding: Hardcover
- 360 pages
Review
"Best-Worst Scaling (BWS) has emerged as a novel and innovative method for eliciting preferences and understanding choice behavior. This book provides researchers and practitioners with a clear understanding of the origins, theory, and use of BWS and contains interesting case studies from a range of disciplines. This excellent collection of papers also provides a fascinating story of how a new research method moves from initial ideas to adoption by researchers in multiple fields worldwide. It is a must-have reference for current users or those interested in learning about BWS."
W. L. (Vic) Adamowicz, Department of Resource Economics and Environmental Sociology, and Research Director, Alberta Land Institute, University of Alberta
"This book is an important guide for researchers, marketing practitioners, and anyone else who wants to apply the power of best-worst scaling to improve the measurement of preferences and attitudes. Louviere, Flynn, and Marley show how best-worst scaling is easy to use with standard tools and software, and can supersede conventional ratings-based and discrete-choice surveys. With this book, the great benefits of best-worst scaling are now within easy reach of everyone."
Scott D. Brown, University of Newcastle, Australia
"This is the definitive source work on best-worst scaling - the method is explained and illustrated by its original developers. A must-have for marketing research practitioners, consultants, and academics interested in the latest advances in stated-choice methods."
Robert J. Meyer, Frederick H. Ecker/Metlife Insurance Professor of Marketing and Co-Director, Wharton Center for Risk Management and Decision Processes, The Wharton School, University of Pennsylvania
About the Author
Jordan J. Louviere is Research Professor at the School of Marketing, University of South Australia Business School. He is particularly known for pioneering work in the design and application of discrete choice experiments (also called 'choice-based conjoint') and he also pioneered best-worst scaling (also known as 'Max-Diff Scaling'). He is co-author of Stated Choice Methods: Analysis and Application (Cambridge University Press, 2000).
Terry N. Flynn Ph.D. is the Director of TF Choices Ltd, UK and Adjunct Fellow at the University of Western Sydney, Australia. He is globally renowned in the use of discrete choice experiments and best-worst scaling in health and allied fields. He is also a world expert in the scoring of quality of life and end-of-life instruments, particularly using BWS, and is a founding member of the International Academy of Health Preference Research.
A. A. J. Marley is Adjunct Professor in the Department of Psychology, University of Victoria, and Research Professor at the Institute for Choice, University of South Australia. He is particularly known for his work in probabilistic models of choice, perception, and voting. He is a co-author of Behavioral Social Choice: Probabilistic Models, Statistical Inference, and Applications (Cambridge University Press, 2006).
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