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| Multivariate Behavioral… | 6 |
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Peer reviewedBurton, 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 reviewedSchaffer, Catherine M.; Green, Paul E. – Multivariate Behavioral Research, 1996
The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…
Descriptors: Cluster Analysis, Comparative Analysis, Marketing, Matrices
Peer reviewedCattell, Raymond B.; Burdsal, Charles A. – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Factor Analysis, Factor Structure, Item Analysis
Peer reviewedKatz, Jeffrey Owen; Rohlf, F. James – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Comparative Analysis, Correlation, Factor Analysis
Peer reviewedNoma, 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 reviewedSchweizer, Karl – Multivariate Behavioral Research, 1992
Two versions of a decision rule for determining the most appropriate number of clusters on the basis of a correlation matrix are presented, applied, and compared with three other decision rules. The new rule is efficient for determining the number of clusters on the surface level for multilevel data. (SLD)
Descriptors: Cluster Analysis, Cluster Grouping, Comparative Analysis, Correlation


