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Principals Versus Osmod: A Comment on Saito and Otsu

Published online by Cambridge University Press:  01 January 2025

Warren F. Kuhfeld and
Forrest W. Young
Warren F. Kuhfeld*
Affiliation:
SAS Institute
Forrest W. Young
Affiliation:
The L. L. Thurstone Psychometric Laboratory, The University of North Carolina
*
Requests for reprints should be sent to Warren F. Kuhfeld, Applications Division, SAS Institute Inc., Box 8000, Cary NC 27512-8000.
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Abstract

Saito and Otsu (1988) compared their OSMOD method of nonmetric principal-component analysis to an early and incorrect implementation of the PRINCIPALS algorithm of Young, Takane, and de Leeuw (1978). In this comment we present results from the current, correct implementations of the algorithm.

Keywords

categorical dataOSMODprincipal component analysisPRINCIPALSPRINQUALMTV algorithm

Information

Type
Notes And Comments
Information
Psychometrika , Volume 54 , Issue 4 , December 1989, pp. 755 - 756
Copyright
Copyright © 1989 The Psychometric Society

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References

Kuhfeld, W. F. (1985). Principal components of ordered categorical data. Unpublished doctoral dissertation. The University of North Carolina.Google Scholar
Saito, T., & Otsu, T. (1988). A method of optimal scaling for multivariate ordinal data and its extensions. Psychometrika, 53, 5–25.CrossRefGoogle Scholar
SAS Institute (1983). Technical Report P-131, Cary, NC: Author.Google Scholar
Tenenhaus, M., & Vachette, J. L. (1977). PRINQUAL: Un programme d'analyse en composantes principales d'un ensemble de variables nominales ou numeriques [A program for a principal component analysis of a set of nominal or numeric variables]. Les Cahiers de Recherche #68, France: CESA, Jouy-en-Josas.Google Scholar
Woodward, J. A., & Overall, J. E. (1976). Factor analysis of rank-ordered data: An old approach revisited. Psychological Bulletin, 83, 864–867.CrossRefGoogle Scholar
Young, F. W., & Kuhfeld, W. F. (1985). PRINQUAL macro. In SAS Institute. The matrix procedure: Language and applications, Cary, NC: Author.Google Scholar
Young, F. W., & Kuhfeld, W. F. (1986). The PRINQUAL procedure: Experimental software for principal components of qualitative data. Preliminary documentation, Cary, NC: SAS Institute.Google Scholar
Young, F. W., Takane, Y., & de Leeuw, J. (1978). The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features. Psychometrika, 43, 279–281.CrossRefGoogle Scholar