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Multivariate Behavioral… | 5 |
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ten Berge, Jos M. F. | 1 |
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Rozeboom, William W. – Multivariate Behavioral Research, 1992
Enriching factor rotation algorithms with the capacity to conduct repeated searches from random starting points can make the tendency to converge to optima that are merely local a way to catch rotations of the input factors that might otherwise elude discovery. Use of the HYBALL computer program is discussed. (SLD)
Descriptors: Algorithms, Comparative Analysis, Factor Analysis, Factor Structure

Belbin, Lee; And Others – Multivariate Behavioral Research, 1992
A method for hierarchical agglomerative polythetic (multivariate) clustering, based on unweighted pair group using arithmetic averages (UPGMA) is compared with the original beta-flexible technique, a weighted average method. Reasons the flexible UPGMA strategy is recommended are discussed, focusing on the ability to recover cluster structure over…
Descriptors: Algorithms, Cluster Analysis, Comparative Analysis, Equations (Mathematics)

ten Berge, Jos M. F.; Zegers, Frits E. – Multivariate Behavioral Research, 1990
Arguments by J. Levin (1988) challenging the convergence properties of the Harman and Jones (1966) method of Minres factor analysis are shown to be invalid. Claims about the invalidity of a rank-one version of the Harman and Jones method are also refuted. (TJH)
Descriptors: Algorithms, Comparative Analysis, Equations (Mathematics), Factor Analysis

Price, Lydia J. – Multivariate Behavioral Research, 1993
The ability of the NORMIX algorithm to recover overlapping population structures was compared to the OVERCLUS procedure and another clustering procedure in a Monte Carlo study. NORMIX is found to be more accurate than other procedures in recovering overlapping population structure when appropriate implementation options are specified. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Comparative Analysis

Dreger, Ralph Mason; And Others – Multivariate Behavioral Research, 1988
Seven data sets (namely, clinical data on children) were subjected to clustering by seven algorithms--the B-coefficient, Linear Typal Analysis; elementary linkage analysis, Numerical Taxonomy System, Statistical Analysis System hierarchical clustering method, Taxonomy, and Bolz's Type Analysis. The little-known B-coefficient method compared…
Descriptors: Algorithms, Children, Clinical Diagnosis, Cluster Analysis