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Simultaneous Classification and Multidimensional Scaling with External Information

Published online by Cambridge University Press:  01 January 2025

Henk A. L. Kiers ,
Donatella Vicari  and
Maurizio Vichi
Henk A. L. Kiers*
Affiliation:
University of Groningen, The Netherlands
Donatella Vicari
Affiliation:
University “La Sapienza” of Rome, Italy
Maurizio Vichi
Affiliation:
University “La Sapienza” of Rome, Italy
*
Requests for reprints should be sent to Henk A.L. Kiers, Heymans Institute (DPMG), University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, THE NETHERLANDS. Email: h.a.l.kiers@rug.nl
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Abstract

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or MDS and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.

Keywords

MDSdissimilarityclassificationmultivariate multiple regressionexternal variables
Type
Original Paper
Information
Psychometrika , Volume 70 , Issue 3 , September 2005 , pp. 433 - 460
Copyright
Copyright © 2005 The Psychometric Society

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