Jordan J. Louviere first proposed best-worst scaling (BWS) in the late 1980s as a way to capitalize on humans' tendency to be more reliable and accurate at identifying extreme options. Louviere first called the method maximum difference scaling, to describe what he hypothesized as the underlying process, namely choosing the pair of stimuli in a set of stimuli that exhibited the largest subjective difference on the underlying continuum of interest. Since that time BWS has been adopted by academics and practitioners in many fields globally. However, marketing researchers continue to refer to it as maximum difference scaling (or “maxdiff”), while academics have overwhelmingly now begun to call it best-worst scaling. Louviere and colleagues changed the name to reflect the fact that years of academic research had made it clear that no one actually used a maximum difference choice process, so a much better general term for the method was BWS.

So, BWS now is almost 25 years old. The current authors began receiving numerous requests for assistance and explanations about how to do BWS around 2005; such requests have continued unabated since then. It became clear from the requests, comments and interactions in BWS and more conventional choice modelling short courses that there was a need for a book that brought BWS theory and methods together in such a way that as many people as possible could learn the basic theory and ways to design, implement and analyze BWS experiments in as simple a pedagogical manner as possible. Therefore, this book began with many discussions between Louviere, Flynn and Marley about the need for such a book, leading to them spending time together in the Seattle, Washington, area in 2009 to begin the writing process. That led to discussions about the need for application chapters, which in turn led to invitations to various researchers, principally academics, who were early adopters of BWS, to contribute such chapters.

So, our key reason for writing the book was to introduce as many people as possible to choice-based measurement methods (of which BWS is one type) with the hope of eventually eliminating the many atheoretical and ad hoc measurement methods that are applied in the social and business disciplines. BWS provides a theoretical framework to measure latent, subjective quantities that can produce measurement values with known properties.