Psychometric Methods in Marketing Research: Part I, Conjoint Analysis

Marketing research, similar to the business disciplines in general, has been a long time borrower of models, tools, and techniques from other sciences. Economists, statisticians, and operations researchers have made significant contributions to marketing, particularly in prescriptive model building. Over the past 30 years, psychometricians and mathematical psychologists have also provided a bounty of research riches in measurement and data analysis techniques. Our editorial comments on those parts of the psychometrician's tool kit that seem most applicable to marketing researchers. Our purview is limited. For example, we do not discuss covariance structure analysis and latent trait models, despite their popularity and utility, and we present a limited coverage of the subareas that we do survey. Here, we focus on conjoint analysis, discussing it in terms of the problems that have motivated its more recent contributions to marketing research. In subsequent editorials, we will consider multidimensional scaling and cluster analysis. Currently, conjoint analysis and the related technique of experimental choice analysis represent the most widely applied methodologies for measuring and analyzing consumer preferences. Note that the seminal theoretical contribution to conjoint analysis was made by Luce, a mathematical psychologist, and Tukey, a statistician (Luce and Tukey 1964). Early psychometric contributions to nonmetric conjoint analysis were also made by Kruskal (1965), Roskam (1968), Carroll (1969, 1973), and Young (1972). The evolution of conjoint analysis in marketing research and practice has been extensively documented in reviews by Green and Srinivasan (1978, 1990), Wittink and Cattin (1989), and Wittink, Vriens, and Burhenne (1994). In addition, Green and Krieger (1993) have surveyed conjoint methodology from the standpoint of new product design and optimization.

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