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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)
Peer reviewed Peer reviewed
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
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Fornell, Claes; And Others – Multivariate Behavioral Research, 1988
This paper shows that redundancy maximization with J. K. Johansson's extension can be accomplished via a simple iterative algorithm based on H. Wold's Partial Least Squares. The model and the iterative algorithm for the least squares approach to redundancy maximization are presented. (TJH)
Descriptors: Algorithms, Equations (Mathematics), Least Squares Statistics, Mathematical Models
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McDonald, Roderick P.; Hartmann, Wolfgang M. – Multivariate Behavioral Research, 1992
An algorithm for obtaining initial values for the minimization process in covariance structure analysis is developed that is more generally applicable for computing parameters connected to latent variables than the currently existing ones. The algorithm is formulated in terms of the RAM model but can be extended. (SLD)
Descriptors: Algorithms, Correlation, Equations (Mathematics), Estimation (Mathematics)
Peer reviewed Peer reviewed
Schweizer, Karl – Multivariate Behavioral Research, 1991
A mathematical formula is introduced for the effect of integrating data. A method is then derived to eliminate the effect from correlations of variables, including mean composites, thus allowing for a clustering algorithm that requires allocation of variables according to the magnitude of their correlations. Examples illustrate the procedure. (SLD)
Descriptors: Algorithms, Classification, Cluster Analysis, Computer Simulation