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Levine, David M. – Multivariate Behavioral Research, 1977
Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)
Descriptors: Cluster Analysis, Information Processing, Multidimensional Scaling, Numbers
Peer reviewed Peer reviewed
Burton, Michael L. – Multivariate Behavioral Research, 1975
Three dissimilarity measures for the unconstrained sorting task are investigated. All three are metrics, but differ in the kind of compensation which they make for differences in the sizes of cells within sortings. Empirical tests of the measures are done with sorting data for occupations names and the names of behaviors, using multidimensional…
Descriptors: Classification, Cluster Analysis, Correlation, Matrices
Peer reviewed Peer reviewed
Jones, Russell A.; Rosenberg, Seymour – Multivariate Behavioral Research, 1974
Descriptors: Cluster Analysis, College Students, Multidimensional Scaling, Organization
Peer reviewed Peer reviewed
Noma, Elliot; Smith, D. Randall – Multivariate Behavioral Research, 1985
Correspondence analysis can provide spatial or clustering representations by assigning spatial coordinates minimizing the distance between individuals linked by a sociometric relationship. These scales may then be used to identify individuals' locations in a multidimensional representation of a group's structure or to reorder the rows and columns…
Descriptors: Cluster Analysis, Goodness of Fit, Matrices, Multidimensional Scaling
Peer reviewed Peer reviewed
Rosenberg, Seymour; Kim, Moonja Park – Multivariate Behavioral Research, 1975
Compares two basic variants of the sorting method: single-sort and multiple sort. The nature of individual differences in sorting, as well as sex differences, were also investigated. Stimulus materials were the 15 mutually exclusive kinship terms selected by Wallace and Atkins (1960). (RC)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, College Students