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A PROC MATRIX program for preference-dissimilarity multidimensional scaling

  • Computational Psychometrics
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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.

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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.

    Google Scholar 

  • Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis.Psychometrika, 29, 1–27.

    Google Scholar 

  • Ramsay, J. O. (1977). Maximum likelihood estimation in multidimensional scaling.Psychometrika, 42, 241–266.

    Google Scholar 

  • Ramsay, J. O. (1978). Confidence regions for multidimensional scaling analysis.Psychometrika, 43, 145–160.

    Google Scholar 

  • Ramsay, J. O. (1980). Joint analysis of direct ratings, pairwise preferences, and dissimilarities.Psychometrika, 45, 149–165.

    Google Scholar 

  • Ramsay, J. O. (1982). Some statistical approaches to multidimensional scaling data (with discussion).Journal of the Royal Statistical Society (Series A),145, 285–312.

    Google Scholar 

  • Ramsay, J. O. (in press). Taking MDS beyond similarity data.Proceedings of SAS User's Group International.

  • SAS Institute. (1982a).SAS Users Guide: Statistics, 1982 Edition. Cary, NC: Author.

    Google Scholar 

  • SAS Institute. (1982b).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, 219–246.

    Google Scholar 

  • Takane, Y. (1981). Multidimensional successive categories scaling: A maximum likelihood method.Psychometrika, 46, 9–28.

    Google Scholar 

  • Takane, Y., & Carroll, J. D. (1981). Nonmetric maximum likelihood multidimensional scaling from directional rankings of similarities,Psychometrika, 46, 389–406.

    Google 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.

    Google Scholar 

  • Torgerson, W. S. (1952). Multidimensional scaling: I. Theory and method.Psychometrika, 17, 401–419.

    Google Scholar 

Download references

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

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Ramsey, J.O. A PROC MATRIX program for preference-dissimilarity multidimensional scaling. Psychometrika 51, 163–170 (1986). https://doi.org/10.1007/BF02294010

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