Hostname: page-component-89b8bd64d-46n74 Total loading time: 0 Render date: 2026-05-06T19:35:35.059Z Has data issue: false hasContentIssue false
  • English
  • Français

A PROC MATRIX Program for Preference-Dissimilarity Multidimensional Scaling

Published online by Cambridge University Press:  01 January 2025

J. O. Ramsey
J. O. Ramsey*
Affiliation:
McGill University
*
Requests for reprints should be sent to J. O. Ramsay, Department of Psychology, 1205 Dr. Penfield Ave., Montreal, Quebec, H3A 1B1, CANADA.
Get access Rights & Permissions [Opens in a new window]

Abstract

A computer program can be a means of communicating the structure of an algorithm as well as a tool for data analysis. From this perspective high-level matrix-oriented languages like PROC MATRIX in the SAS system are especially useful because of their readability and compactness. An algorithm for the joint analysis of dissimilarity and preference data using maximum likelihood estimation is presented in PROC MATRIX code.

Keywords

Data AnalysisComputer ProgramPublic PolicyMaximum Likelihood EstimationLikelihood Estimation

Information

Type
Computational Psychometrics
Information
Psychometrika , Volume 51 , Issue 1 , March 1986 , pp. 163 - 170
Copyright
Copyright © 1986 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

This research was supported by Grant APA 3020 from the Natural Sciences and Engineering Research Council of Canada.

References

Carroll, J. D., Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling viaN-way generalization of Eckart-Young decomposition. Psychometrika, 35, 283–320.CrossRefGoogle Scholar
Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.CrossRefGoogle Scholar
Ramsay, J. O. (1977). Maximum likelihood estimation in multidimensional scaling. Psychometrika, 42, 241–266.CrossRefGoogle Scholar
Ramsay, J. O. (1978). Confidence regions for multidimensional scaling analysis. Psychometrika, 43, 145–160.CrossRefGoogle Scholar
Ramsay, J. O. (1980). Joint analysis of direct ratings, pairwise preferences, and dissimilarities. Psychometrika, 45, 149–165.CrossRefGoogle Scholar
Ramsay, J. O. (1982). Some statistical approaches to multidimensional scaling data (with discussion). Journal of the Royal Statistical Society, 145, 285–312.CrossRefGoogle Scholar
Ramsay, J. O. (in press). Taking MDS beyond similarity data. Proceedings of SAS User's Group International.Google Scholar
SAS Institute (1982). SAS Users Guide: Statistics 1982 Edition,, Cary, NC: Author.Google Scholar
SAS Institute (1982). SAS User's Guide: Basics 1982 Edition,, Cary, NC: Author.Google Scholar
Shepard, R. N. (1962). Analysis of proximities: Multidimensional scaling within unknown distance function. I and II. Psychometrika, 27, 125–140.CrossRefGoogle Scholar
Takane, Y. (1981). Multidimensional successive categories scaling: A maximum likelihood method. Psychometrika, 46, 9–28.CrossRefGoogle Scholar
Takane, Y., Carroll, J. D. (1981). Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities. Psychometrika, 46, 389–406.CrossRefGoogle Scholar
Takane, Y., Young, F. W., de Leeuw, J. (1977). Nonmetric individual differences multidimensional scaling: Alternating least squares method with optimal scaling features. Psychometrika, 42, 7–67.CrossRefGoogle Scholar
Torgerson, W. S. (1952). Multidimensional scaling: I. Theory and method. Psychometrika, 17, 401–419.CrossRefGoogle Scholar