Introduction to Multidimensional Scaling and Its Applications

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Abstract
Although Richardson (1938) and Young and Householder (1938) may have officially initiated the multidimensional scaling (MDS) literature in psychology, frequent applications did not begin to appear until the seminal papers on nonmetric MDS by Shepard (1962) and Kruskal (1964). Twenty years later, it is time to critically examine the MDS literature and its contribution to psychology. The first two papers in this special issue review statistical developments in MDS with an emphasis on the design of MDS studies. The last four papers scrutinize the MDS research in four areas of common application: consumer, social, cognitive, and vocational psychology. Carroll and Arabie (1980) have described two ways to define MDS. According to the broader of the two definitions, MDS means a set of techniques for estimating parameters in geometric models so as to yield a representation of data structure. Such a broad definition would encompass cluster, discriminant, and factor analysis. These techniques are treated here as alternatives to MDS, rather than as methods included within it. In this special issue, the MDS literature refers to a body of knowledge involving (1) a set of statistical techniques for estimating the parameters in and assessing the fit of various spatial distance models for proximity or preference data and (2) the coordinate representations of stimulus structure that result from such statistical techniques. This introduction first briefly reviews the past 50 years of developments in MDS, developments covered more extensively by Coxon (1982), Davison (1993), Kruskal and Wish (1978), and Schiffman, Reynolds, and Young (1981). Then it summarizes the six papers that follow