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Multidimensional Unfolding: Determining Configuration from Complete Rank Order Preference Data

Published online by Cambridge University Press:  01 January 2025

William L. Hays  and
Joseph F. Bennett
William L. Hays
Affiliation:
University of Michigan
Joseph F. Bennett
Affiliation:
Lincoln Laboratory, Massachusetts Institute of Technology
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Abstract

Within the model of isotonic space, a principle is presented which generalizes the unfolding technique to the multidimensional case. The availability of exhaustive configurational solutions given complete data is pointed out. Finally three criteria are suggested for the choice of a particular solution from among the set of all solutions, which are applicable in the case either of complete or incomplete data.

Information

Type
Original Paper
Information
Psychometrika , Volume 26 , Issue 2 , June 1961 , pp. 221 - 238
Copyright
Copyright © 1961 The Psychometric Society

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Footnotes

*

Deceased.

References

Bennett, J. F. Determination of the number of independent parameters of a score matrix from the examination of rank orders. Psychometrika, 1956, 21, 383–393.CrossRefGoogle Scholar
Bennett, J. F. and Hays, W. L. Multidimensional unfolding: determining the dimensionality of ranked preference data. Psychometrika, 1960, 25, 27–43.CrossRefGoogle Scholar
Coombs, C. H. Psychological scaling without a unit of measurement. Psychol. Rev., 1950, 57, 145–158.CrossRefGoogle ScholarPubMed
Coombs, C. H. A theory of psychological scaling. Engng. Res. Bull., Ann Arbor, Mich.: Univ. Michigan Press, 1952.Google Scholar
Thurstone, L. L. Multiple-factor analysis, Chicago: Univ. Chicago Press, 1947.Google Scholar