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| Psychometrika | 7 |
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| Bentler, P. N. | 1 |
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Peer reviewedTyler, David E. – Psychometrika, 1982
The index of redundancy is a measure of association between a set of independent variables and a set of dependent variables. Properties and interpretations of redundancy variables, in a particular subset of the original variables, are discussed. (JKS)
Descriptors: Correlation, Data Analysis, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedJohansson, J. K. – Psychometrika, 1981
An extension of Wollenberg's redundancy analysis (an alternative to canonical correlation) is proposed to derive Y-variates corresponding to the optimal X-variates. These variates are maximally correlated with the given X-variates, and depending upon the standardization chosen they also have certain properties of orthogonality. (Author/JKS)
Descriptors: Correlation, Mathematical Models, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedMuller, Keith E. – Psychometrika, 1981
Redundancy analysis is an attempt to provide nonsymmetric measures of the dependence of one set of variables on another set. This paper attempts to clarify the nature of redundancy analysis and its relationships to canonical correlation and multivariate multiple linear regression. (Author/JKS)
Descriptors: Correlation, Data Analysis, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedCramer, Elliot M.; Nicewander, W. Alan – Psychometrika, 1979
A distinction is drawn between redundancy measurement and the measurement of multivariate association between two sets of variables. Several measures of multivariate association between two sets of variables are examined. (Author/JKS)
Descriptors: Correlation, Measurement, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedSkinner, C. J. – Psychometrika, 1984
Multivariate selection can be represented as a linear transformation in a geometric framework. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation. (Author/BW)
Descriptors: Factor Analysis, Geometric Concepts, Mathematical Formulas, Multiple Regression Analysis
Peer reviewedBentler, P. N.; Freeman, Edward H. – Psychometrika, 1983
Interpretations regarding the effects of exogenous and endogenous variables on endogenous variables in linear structural equation systems depend upon the convergence of a matrix power series. The test for convergence developed by Joreskog and Sorbom is shown to be only sufficient, not necessary and sufficient. (Author/JKS)
Descriptors: Data Analysis, Mathematical Models, Matrices, Multiple Regression Analysis
Peer reviewedTenenhaus, Michel – Psychometrika, 1988
Canonical analysis of two convex polyhedral cones involves looking for two vectors whose square cosine is a maximum. New results about the properties of the optimal solution to this problem are presented. The convergence of an alternating least squares algorithm and properties of limits of calculated sequences are discussed. (SLD)
Descriptors: Algorithms, Analysis of Variance, Graphs, Least Squares Statistics
