We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
Forrest W. Young. Multidimensional Scaling: History, Theory, and Applications. Edited by R. M. Hamer. Hillsdale, NJ: Lawrence Erlbaum, 1987, XVII + 317 pp.
Review products
Forrest W. Young. Multidimensional Scaling: History, Theory, and Applications. Edited by R. M. Hamer. Hillsdale, NJ: Lawrence Erlbaum, 1987, XVII + 317 pp.
Published online by Cambridge University Press:
01 January 2025
An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
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.)
References
d'Aubigny, G. (1989). L'Analyse Multidimensionnelle des Tableaux de Dissimilarité [Multivariate analysis of dissimilarity tables], Grenoble, France: Université Joseph Fourier.Google Scholar
Bentler, P. M., Weeks, D. G. (1978). Restricted multidimensional scaling models. Journal of Mathematical Psychology, 17, 138–151.CrossRefGoogle Scholar
Barthelemy, J.-P., Guenoche, A. (1988). Les Arbres et les Représentations des Proximités [Trees and representations of proximities], Paris: Masson.Google Scholar
Borg, I. (1981). Multidimensional data representations: When and why?, Ann Arbor, MI: Mathesis Press.Google Scholar
Borg, I., Lingoes, J. C. (1980). A model and algorithm for multidimensional scaling with external constraints on the distances. Psychometrika, 45, 25–38.CrossRefGoogle Scholar
Borg, I., Lingoes, J. C. (1987). Multidimensional similarity structure analysis, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Brady, H. E. (1985). Statistical constistency and hypothesis testing for nonmetric multidimensional scaling. Psychometrika, 50, 509–537.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H. (1985). Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association, 80, 580–619.CrossRefGoogle Scholar
Carroll, J. D. (1983). Common dimension analysis. Paper presented at the Multidimensional Data Analysis Workshop, Paris.Google Scholar
Constantine, A. G., Gower, J. C. (1971). Graphical representation of asymmetric matrices. Applied Statistics, 7, 297–304.Google Scholar
Davidson, M. L. (1983). Multidimensional scaling, New York: J. Wiley and Sons.Google Scholar
de Leeuw, J., Heiser, W. (1982). Theory of multidimensional scaling. In Krishnaiah, P. R., Kanal, L. (Eds.), Handbook of statistics (pp. 285–316). Amsterdam: North Holland.Google Scholar
Gower, J. C. (1966). Some distance properties of latent roots and vectors used in multivariate analysis. Biometrika, 53, 325–338.CrossRefGoogle Scholar
Green, P. E., Carmone, F. J. (1970). Multidimensional scaling and related techniques in marketing analysis, Boston: Allyn and Bacon.Google Scholar
Hartmann, W. (1979). Geometrische modelle zur analyse empirischer daten [Geometric models for the analysis of empirical data], Berlin: Akademie Verlag.CrossRefGoogle Scholar
Hayashi, C. (1952). On a prediction of phenomena from qualitative data and quantification of qualitative data from the mathematico-statistical point of view. Annals of the Institute of Statistical Mathematics, 4, 19–30.Google Scholar
Kruskal, J. B., Wish, M. (1978). Multidimensional scaling, Beverly Hills: Sage.CrossRefGoogle Scholar
Lee, Sik-Yum, Bentler, P. M. (1980). Functional relations in multidimensional scaling. British Journal of Mathematical and Statistical Psychology, 33, 142–150.CrossRefGoogle Scholar
McDonald, R. P. (1975). Descriptive axioms for common factor theory. Image theory and component theory. Psychometrika, 40, 137–152.CrossRefGoogle Scholar
Meulman, J. (1986). A distance approach to nonlinear multivariate analysis, Leiden: DSWO Press.Google Scholar
Ramsay, J. O. (1982). Some statistical approaches to multidimensional scaling data. Journal of the Royal Statistical Society, Series A, 145(3), 285–311.CrossRefGoogle Scholar
Roskam, E. E. (1968). Metric analysis of ordinal data in pychology: Models and numerical methods for metric analysis of conjoint ordinal data in psychology. Van Voorshotten, University of Leiden Press.Google Scholar
Saito, T. (1986). Multidimensional scaling to explore complex aspects in dissimilarity judgment. Behaviormetrika, 20, 35–62.CrossRefGoogle Scholar
Schiffman, S. S., Reynolds, M. L., Young, F. W. (1981). Introduction to Multidimensional scaling: Theory, methods and applications, New York: Academic Press.Google Scholar
Takane, Y. (1977). Statistical procedures for non metric multidimensional scaling. (Doctoral dissertation, University of North Carolina, Chapel Hill.)University Microfilm International, 78-7169.Google Scholar
Winsberg, S. & Carroll, J. D. (1984, June). A nonmetric method for a multidimensional scaling model postulating common and specific dimension. Paper presented at the Annual Meeting of the Psychometric Society.Google Scholar
Young, F. W., de Leeuw, J., Takane, Y. (1976). The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features. Psychometrika, 43, 279–281.CrossRefGoogle Scholar