14 Multidimensional scaling and its applications
Handbook of statisticsPublished 1 January 1982
Myron Wish, J. Douglas Carroll
Citations43
SJR quartileQ4
SJR score0.21
SNIP0.11
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TL;DR
The aim of MDS is to discover the number of dimensions appropriate for the data and to locate the stimulus objects on each dimension to determine the dimengionahty and the configuration of stimuli in the multidimensional space.
Abstract
Multidimensional Scaling (MDS) is a general term for a class of techniques that can be used to develop spatial representations of proximities among psychological stimuli or other entities. These techniques involve iterative algorithms, usually requiring high-speed computers, for discovering and displaying the underlying structure of the data matrix. The aim of MDS is to discover the number of dimensions appropriate for the data and to locate the stimulus objects on each dimension — that is, to determine the dimengionahty and the configuration of stimuli in the multidimensional space. There is a wide variety of methods for obtaining data appropriate for MDS. The most direct way is to ask subjects to give pairwise ratings or to sort stimulus objects according to their similarity, relatedness and association. Some other sources of proximities are “confusions” from a stimulus identification or “same-different” task, co-occurrences of stimuli in text or other material, indices of communication or volume flow, and various measures of profile distance derived from objective or subjective multivariate data.
Keywords
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