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.
P. Arabie, J. Douglas Carroll and Wayne S. DeSarbo. Three-Way Scaling and Clustering. Newbury Park: Sage Publications, 1987, ISBN 0-8039-3068-2, 92 pp. (Quantitative Applications in the Social Sciences #65.)
Review products
P. Arabie, J. Douglas Carroll and Wayne S. DeSarbo. Three-Way Scaling and Clustering. Newbury Park: Sage Publications, 1987, ISBN 0-8039-3068-2, 92 pp. (Quantitative Applications in the Social Sciences #65.)
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
Carroll, J. D., Arabie, P. (1983). INDCLUS: An individual differences generalization of the ADCLUS model and the MAPCLUS algorithm. Psychometrika, 48, 157–169.CrossRefGoogle 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, 283–319.CrossRefGoogle Scholar
de Leeuw, J. (1980). Majorization algorithms for individual differences in multidimensional scaling. Paper presented at the Symposium on Multidimensional Scaling and Interindividual differences held at the XXIInd International Congress of Psychology, Leipzig.Google Scholar
Heiser, W. J., & Stoop, I. (1986). Explicit SMACOF algorithms for individual differences scaling (Report RR-86-14). University of Leiden, Department of Data Theory.Google Scholar
Hubert, L. J., Golledge, R. G., Constanza, C. M., Gale, N., Halperin, W. C. (1984). Nonparametric tests for directional data. In Bahrenberg, G., Fischer, M. M., Nijkamp, P. (Eds.), Recent developments in spatial data analysis (pp. 171–189). Aldershot, England: Gower.Google Scholar
Jones, C. L. (1983). A note on the use of directional statistics in weighted Euclidean distances multidimensional scaling models. Psychometrika, 48, 473–476.CrossRefGoogle Scholar
Kruskal, J. B., Wish, M. (1978). Multidimensional scaling, Beverly Hills: Sage Publications.CrossRefGoogle Scholar
Miller, G. A., Nicely, P. E. (1955). An analysis of perceptual confusions among some English consonants. Journal of the Acoustical Society of America, 27, 338–352.CrossRefGoogle Scholar
Ramsay, J. O. (1977). Maximum likelihood estimation in multidimensional scaling. Psychometrika, 42, 241–266.CrossRefGoogle Scholar
Rosenberg, S., Kim, M. P. (1975). The method of sorting as a data-gathering procedure in multivariate research. Multivariate Behavioral Research, 10, 489–502.CrossRefGoogle ScholarPubMed
Shepard, R. N., Arabie, P. (1979). Additive clustering: Representation of similarities as combinations of discrete overlapping properties. Psychological Review, 86, 87–123.CrossRefGoogle Scholar
Schiffman, S. S., Reynolds, M. L., Young, F. W. (1981). Introduction to multidimensional scaling, New York: Academic Press.Google Scholar
Takane, Y., Young, F. W., de Leeuw, J. (1977). Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features. Psychometrika, 42, 7–67.CrossRefGoogle Scholar