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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.

Recent theoretical developments in the field of absolute identification have stressed differences between relative and absolute processes, that is, whether stimulus magnitudes are judged relative to a shorter term context provided by recently presented stimuli or a longer term context provided by the entire set of stimuli. The authors developed a model (SAMBA: selective attention, mapping, and ballistic accumulation) that integrates shorter and longer term memory processes and accounts for both the choices made and the associated response time distributions, including sequential effects in each. The model's predictions arise as a consequence of its architecture and require estimation of only a few parameters with values that are consistent across numerous data sets. The authors show that SAMBA provides a quantitative account of benchmark choice phenomena in classical absolute identification experiments and in contemporary data involving both choice and response time.Keywords: absolute identification, absolute identification models, response time, response time distributions, sequential effects Performance in absolute identification tasks has fascinated researchers for over 50 years (e.g., Garner, 1953;Miller, 1956;Pollack, 1952Pollack, , 1953. Research in the past 35 years has emphasized both data and formal theories (e.g., Braida & Durlach, 1972;Durlach & Braida, 1969;Laming, 1984;Lockhead, 2004;Luce, Nosofsky, Green, & Smith, 1982;Marley & Cook, 1984;Petrov & Anderson, 2005;Stewart, Brown, & Chater, 2005;Treisman & Williams, 1984) and, most recently, has been concerned with both the choices made and the time it takes to make them (Kent & Lamberts, 2005;Lacouture & Marley, 1991, 1995, 2004. As Shiffrin and Nosofsky (1994, p. 358) stated in an article reassessing the significance of Miller's (1956) classic paper, "absolute identification has captured the imagination . . . not only because the empirical results are so startling . . . but also because [they] provide perplexing problems for classic psychophysical models." Luce (1986, chapter 10) gave an excellent summary of data and theory to that date, and Lockhead (2004) summarized data and theory most relevant to relative interpretations of absolute identification, where the relativity is with respect to stimuli and responses from previous trials. Stewart et al. (2005) and Petrov and Anderson (2005) provided comprehensive reviews of choice data and the related theory, with emphasis on theoretical approaches over the past 20 years.A typical absolute identification task requires a participant to identify, on each trial, which stimulus has been presented from a relatively small prespecified set. In general, people are unable to accurately identify more than about 8 -10 stimuli that vary on a single physical dimension. For example, the stimuli might be a set of 10 lines varying only in length, with the shortest line labeled 1 and the longest 10. A participant previews the entire labeled set and is then shown the lines one at time, over numerous trials, and asked t...

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