Hostname: page-component-5f745c7db-hj587 Total loading time: 0 Render date: 2025-01-06T21:15:38.808Z Has data issue: true hasContentIssue false

Analysis of Asymmetry by a Slide-Vector

Published online by Cambridge University Press:  01 January 2025

Berrie Zielman
Affiliation:
Department of Data Theory, University of Leiden
Willem J. Heiser*
Affiliation:
Department of Data Theory, University of Leiden
*
Requests for reprints should be sent to Willem J. Heiser, Department of Data Theory, University of Leiden, PO Box 9555, 2300 RB Leiden, THE NETHERLANDS.

Abstract

The slide-vector scaling model attempts to account for the asymmetry of a proximity matrix by a uniform shift in a fixed direction imposed on a symmetric Euclidean representation of the scaled objects. Although no method for fitting the slide-vector model seems available in the literature, the model can be viewed as a constrained version of the unfolding model, which does suggest one possible algorithm. The slide-vector model is generalized to handle three-way data, and two examples from market structure analysis are presented.

Type
Original Paper
Copyright
Copyright © 1993 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.)

Footnotes

The authors wish to thank Ivo van der Lans, John Gower, and the Editor for their comments on an earlier version of this manuscript.

References

Carroll, J. D. (1972). Individual differences and multidimensional scaling. In Shepard, R. N., Romney, A. K., Nerlove, S. B. (Eds.), Multidimensional scaling: Theory and applications in the behavioral sciences (pp. 105155). New York: Seminar Press.Google Scholar
Carroll, J. D. (1981). Models and methods for multidimensional analysis of preferential choice (or other dominance) data. In Lantermann, E. D., Feger, H. (Eds.), Similarity and choice (pp. 234289). Bern: Huber.Google Scholar
Carroll, J. D., Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika, 35, 283319.CrossRefGoogle Scholar
Carroll, J. D., Wish, M. (1974). Multidimensional perceptual models and measurement methods. In Carterette, E. C., Friedman, M. P. (Eds.), Handbook of perception (pp. 391447). New York: Academic Press.Google Scholar
Caussinus, H. (1965). Contribution a l'analyse statstique des tableaux de correlation [Contributions to the statistical analysis of correlation matrices]. Annals of the Faculty of Science, University of Toulouse, 29, 77182.CrossRefGoogle Scholar
Constantine, A. G., Gower, J. C. (1978). Graphical representation of asymmetric matrices. Journal of the Royal Statistical Society, Series C, 27, 297304.Google Scholar
Coxon, A. P. M. (1982). The user's guide to multidimensional scaling, London: Heinemann Educational Books.Google Scholar
de Leeuw, J. (1988). Convergence of the majorization method for multidimensional scaling. Journal of Classification, 5, 163180.CrossRefGoogle Scholar
de Leeuw, J., Heiser, W. (1980). Multidimensional scaling with restrictions on the configuration. In Krishnaiah, P. R. (Eds.), Multivariate analysis-V (pp. 501522). Amsterdam: North Holland.Google Scholar
de Leeuw, J., Heiser, W. J. (1982). Theory of multidimensional scaling. In Krishnaiah, P. R., Kanal, L. N. (Eds.), Handbook of statistics, Vol. 2 (pp. 285316). Amsterdam: North Holland.Google Scholar
Gower, J. C. (1977). The analysis of asymmetry and orthogonality. In Barra, J. R., Brodeau, F., Romer, G., van Cutsem, B. (Eds.), Recent developments in statistics (pp. 109123). Amsterdam: North Holland.Google Scholar
Harshman, R. A., Green, P. E., Wind, Y., Lundy, M. E. (1982). A model for the analysis of asymmetric data in marketing research. Marketing Science, 1, 204242.CrossRefGoogle Scholar
Heiser, W. J. (1987). Joint ordination of species and sites: the unfolding technique. In Legendre, P., Legendre, L. (Eds.), Developments in numerical ecology (pp. 189221). Berlin: Springer Verlag.CrossRefGoogle Scholar
Heiser, W. J., Meulman, J. (1983). Analyzing rectangular tables by joint and constrained multidimensional scaling. Journal of Econometrics, 22, 139167.CrossRefGoogle Scholar
Heiser, W. J., Meulman, J. (1983). Constrained multidimensional scaling, including confirmation. Applied Psychological Measurement, 7, 381404.CrossRefGoogle Scholar
Heiser, W. J., Stoop, I. (1986). Explicit SMACOF algorithms for individual differences scaling, Leiden: University of Leiden, Department of Data Theory.Google Scholar
Holman, E. W. (1979). Monotonic models for asymmetric proximities. Journal of Mathematical Psychology, 20, 115.CrossRefGoogle Scholar
Keeren, G., Baggen, S. (1981). Recognition models of alphanumeric characters. Perception and Psychophysics, 29, 234246.CrossRefGoogle Scholar
Krumhansl, C. L. (1978). Concerning the applicability of geometric models to similarity data: The interrelationship between similarity and spatial density. Psychological Review, 84, 445463.CrossRefGoogle Scholar
Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 127.CrossRefGoogle Scholar
Kruskal, J. B. (1964). Nonmetric multidimensional scaling: A numerical method. Psychometrika, 29, 115129.CrossRefGoogle Scholar
Meulman, J., Heiser, W. J. (1984). Constrained multidimensional scaling: More directions than dimensions. In Havránek, T., Šidák, Z., Novák, M. (Eds.), Compstat 1984, Proceedings in computational statistics (pp. 5156). Vienna: Physica Verlag.Google Scholar
Milligan, G. W. (1980). An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45, 325342.CrossRefGoogle Scholar
Nosofsky, R. M. (1991). Stimulus bias, asymmetric similarity, and classification. Cognitive Psychology, 23, 94140.CrossRefGoogle Scholar
Okada, A. (1988). Asymmetric multidimensional scaling of car switching data. In Gaul, W., Schader, M. (Eds.), Data, expert knowledge and decisions (pp. 279290). Berlin: Springer Verlag.CrossRefGoogle Scholar
Okada, A. (1988). An analysis of intergenerational occupational mobility by asymmetric multidimensional scaling (pp. 115). Groningen: University of Groningen.Google Scholar
Tobler, W. R., Wineburg, S. (1971). A Cappadocian speculation. Nature, 231, 3942.CrossRefGoogle ScholarPubMed
Weeks, D. G., Bentler, P. M. (1982). Restricted multidimensional scaling models for asymmetric proximities. Psychometrika, 47, 201208.CrossRefGoogle Scholar
Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327352.CrossRefGoogle Scholar