Abstract
Publisher Summary Multidimensional scaling (MDS) is a set of data analysis techniques for the analysis of data. Two types of definitions of MDS exist—namely, the narrow and broad. This chapter provides a narrow view of MDS. According to this view, MDS is a collection of techniques that represent proximity data by spatial distance models. The chapter provides examples of MDS by the simple Euclidean distance model and discusses the applications of MDS by the weighted Euclidean distance model to capture certain individual differences in proximity judgments. Simple MDS is typically applied to a single (dis)similarity matrix. Most of the results are derived by least squares (LS) MDS. Maximum likelihood (ML) MDS and what it can do in the analysis of proximity data have been described in the chapter. It also illustrates the applications of unfolding analysis, a special kind of MDS to represent individual differences in preference judgments. with the development of more flexible and more reliable algorithms for MDS and unfolding analysis, it is expected that its applications will grow faster both in number and variety.
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