First systematic treatment of best-worst scaling, explaining how to implement, analyze, and apply the theory across a range of disciplines.
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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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Hardback. Condition: New. 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. Seller Inventory # LU-9781107043152
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