Abstract
Multidimensional scaling (MDS) is a set of data analysis techniques used to explore the structure of (dis)similarity data. MDS represents a set of objects as points in a multidimensional space in such a way that the points corresponding to similar objects are located close together, while those corresponding to dissimilar objects are located far apart. The investigator then attempts to make sense of the derived object configuration by identifying meaningful regions and/or directions in the space. In this article, we first introduce the basic concepts and models of MDS. We then discuss a variety of (dis)similarity data and their scale levels, and the kinds of MDS techniques to be used in specific situations such as individual differences MDS and unfolding analysis.
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