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Single Subject Incomplete Designs for Nonmetric Multidimensional Scaling

Published online by Cambridge University Press:  01 January 2025

Ian Spence  and
Dennis W. Domoney
Ian Spence*
Affiliation:
University of Western Ontario
Dennis W. Domoney
Affiliation:
University of Western Ontario
*
Requests for reprints should be addressed to the first author at the Department of Psychology, University of Western Ontario, London, Ontario N6A 3K7, Canada.
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Abstract

Monte Carlo procedures were used to investigate the properties of a nonmetric multidimensional scaling algorithm when used to scale an incomplete matrix of dissimilarities. Various recommendations for users who wish to scale incomplete matrices are made: (a) recovery was found to be satisfactory provided that the “degrees of freedom” ratio exceeded 3.5, irrespective of error level; (b) cyclic designs were found to provide best recovery, although random patterns of deletion performed almost as well; and (c) strongly locally connected designs, specifically overlapping cliques, were generally inferior. These conclusions are based on 837 scaling solutions and are applicable to stimulus sets containing more than 30 objects.

Type
Original Paper
Information
Psychometrika , Volume 39 , Issue 4 , December 1974 , pp. 469 - 490
Copyright
Copyright © 1974 The Psychometric Society

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Footnotes

‡

This research was supported by grant A8351 from the National Research Council of Canada to the first author, and is based in part on the M. A. thesis of the second author. The assistance, advice and criticisms of John C. Ogilvie and John Vranch are gratefully acknowledged.

*

Now at the University of Toronto.

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