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Peer reviewedMcQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1975
A rapid method for hierarchically clustering the n objects of a matrix which portrays the interrelation of every object to every other object, where n equals any number up to 1,000 and even larger, is developed and discussed. Results compare favorably with those from other excellent methods. (Author/BJG)
Descriptors: Cluster Analysis, Comparative Analysis, Evaluation Methods, 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 reviewedKatz, Jeffrey Owen; Rohlf, F. James – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Comparative Analysis, Correlation, Factor Analysis
Peer reviewedMcQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1976
A relatively reliable and valid hierarchy of clusters of objects is plotted from the highest column entries, exclusively, of a matrix of interassociations between the objects. Having developed out of a loose definition of types, the method isolates both loose and highly definitive types, and all those in between. (Author/RC)
Descriptors: Cluster Analysis, Cluster Grouping, Comparative Analysis, Data Analysis
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
Peer reviewedShepard, Roger N. – Psychometrika, 1974
Six major problems confronting attempts to use nonmetric multidimensional scaling to represent structures underlying similarity data are identified and the author's prospects for over-coming each of these problems are presented. (RC)
Descriptors: Cluster Analysis, Comparative Analysis, Data Analysis, Goodness of Fit
PDF pending restorationRudnitsky, Alan N. – 1977
Three approaches to the graphic representation of similarity and dissimilarity matrices are compared and contrasted. Specifically, Kruskal's multidimensional scaling, Johnson's hierarchical clustering, and Waern's graphing techniques are employed to depict, in two dimensions, data representing the structure of a set of botanical concepts. Each of…
Descriptors: Botany, Cluster Analysis, Cluster Grouping, Comparative Analysis
Peer reviewedRay, Michael L.; Heeler, Roger M. – Educational and Psychological Measurement, 1975
Methods for analyzing the multitrait-multimethod matrix are reviewed and the results of their application to a classic data set are compared. It is shown that different analysis methods can yield different validity conclusions, and that the results obtained are partly dependent on the subjective judgments of the users. (Author/RC)
Descriptors: Cluster Analysis, Comparative Analysis, Factor Analysis, Goodness of Fit
Peer reviewedDoreian, Patrick – Journal of the American Society for Information Science, 1985
Two journal-to-journal matrices for psychology in 1950 and 1960 are analyzed in terms of structural equivalence to test following hypotheses: that journals of a discipline function as a status-role relational system; that interdisciplinary journals are distant from journals of a field; that journal networks have a core-periphery structure. (26…
Descriptors: Citations (References), Cluster Analysis, Comparative Analysis, Hypothesis Testing


