The Four Purposes of Multidimensional Scaling

Abstract

Multidimensional scaling (MDS) is a method that represents measurements of similarity (or dissimilarity) among pairs of objects as distances between points of a low-dimensional multidimensional space. The data, for example, may be correlations among intelligence tests, and the MDS representation is a plane that shows the tests as points that are closer together the more positively the tests are correlated. The graphical display of the correlations provided by MDS enables the data analyst to literally “look” at the data and to explore their structure visually. This often shows regularities that remain hidden when studying arrays of numbers. Another application of MDS is to use some of its mathematics as models for dissimilarity judgments. For example, given two objects of interest, one may explain their perceived dissimilarity as the result of a mental arithmetic that mimics the distance formula. According to this model, the mind generates an impression of dissimilarity by adding up the perceived differences of the two objects over their properties.