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| Multivariate Behavioral… | 31 |
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Peer reviewedTakane, Yoshio; Hwang, Heungsun – Multivariate Behavioral Research, 2002
Proposes a method for generalized constrained canonical correlation analysis (GCCANO) that incorporates external information on both rows and columns of data matrices. In this method, each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical correlation analysis is…
Descriptors: Correlation, Matrices, Statistical Analysis
Peer reviewedNicewander, W. Alan – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Factor Analysis, Matrices, Statistics
Peer reviewedOgasawara, Haruhiko – Multivariate Behavioral Research, 1999
Derives the asymptotic standard errors and intercorrelations for several matrix correlations assuming multivariate normality for manifest variables and derives the asymptotic standard errors of the matrix correlations for two factor-loading matrices. (SLD)
Descriptors: Correlation, Error of Measurement, Matrices
Peer reviewedKaiser, Henry F. – Multivariate Behavioral Research, 1974
A desirable property of the equamax criterion for analytic rotation in factor analysis is presented. (Author)
Descriptors: Correlation, Factor Analysis, Matrices, Orthogonal Rotation
Peer reviewedCramer, Elliot M. – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedJoe, George W.; Woodward, J. Arthur – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Matrices, Sampling, Statistical Analysis
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 reviewedHumphreys, Lloyd G.; Montanelli, Richard G. – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Factor Analysis, Matrices, Sampling
Peer reviewedKaiser, Henry F.; Horst, Paul – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Error of Measurement, Factor Analysis, Matrices
Peer reviewedSteiger, James H. – Multivariate Behavioral Research, 1980
The goodness-of-fit of correlational pattern hypotheses has traditionally been assessed either with a likelihood ratio statistic or with a quadratic form statistic. Several alternative statistics, based on the use of the Fisher r-to-z transform, are proposed and assessed in a Monte Carlo experiment. (Author/JKS)
Descriptors: Correlation, Data Analysis, Hypothesis Testing, Longitudinal Studies
Peer reviewedCramer, Elliot M. – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Multivariate Analysis
Peer reviewedReichardt, Charles S.; Coleman, S. C. – Multivariate Behavioral Research, 1995
The criteria for assessing convergent and discriminant validity proposed by D. T. Campbell and D. W. Fiske (1959) are shown to be inadequate for either the additive or multiplicative structures of data in a multitrait-multimethod matrix. Model-specific criteria are more promising for assessing convergent and discriminant validity. (Author/SLD)
Descriptors: Correlation, Criteria, Evaluation Methods, Matrices
Peer reviewedGolding, Stephen L.; Seidman, Edward – Multivariate Behavioral Research, 1974
A relatively simple technique for assessing the convergence of sets of variables across method domains is presented. The technique, two-step principal components analysis, empirically orthogonalizes each method domain into sets of components, and then analyzes convergence among components across domains. (Author)
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Factor Structure
Peer reviewedBarcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1975
Results showed that the canonical correlations are very stable upon replication. The results also indicated that there is no solid evidence for concluding that components are superior to the coefficients, at least not in terms of being more reliable. (Author/BJG)
Descriptors: Correlation, Factor Analysis, Matrices, Monte Carlo Methods
Peer reviewedCrawford, Charles B.; DeFries, J. C. – Multivariate Behavioral Research, 1978
The application of component analysis to phenotypic, genetic, and environmental correlation matrices is discussed. Formulas for computation of component scores and the interpretation of factors is discussed. An example is presented. (Author/JKS)
Descriptors: Correlation, Environmental Research, Factor Analysis, Genetics


