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Fig. 3.40 An MDS-configured base map of topical statements and ratings of importance shown
as stacked bars
the principal coordinates, which would be obtained when doing PCA on A. This
approach is called principal coordinates analysis , or classical scaling. A more
detailed account of this correspondence can be found in Everitt and Rabe-Hesketh
( 1997 ).
Correspondence analysis is classically used on a two-way contingency table
in order to visualize the relations between the row and column categories. The
unfolding models do the same: subjects (row-categories) and objects (column-
categories) are visualized in a way that the order of the distances between a
subject-point and the object-points reflects the preference ranking of the subject.
The measure of “proximity” used in CA is the Chi-square distance between the
profiles. A short description of CA and its relation to MDS can be found in Borg
and Groenen ( 1997 ).
Cluster analysis models are equally applicable to proximity data including two-
way (asymmetric) square and rectangular data as well as three-way two-mode data.
The main difference with the MDS models is that most models for cluster analysis
lead to a hierarchical structure. Path distances under a number of restrictions
approach the dissimilarities.
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