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Cramer, Elliot M. – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Multivariate Analysis
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Cerny, Barbara A.; Kaiser, Henry F. – Educational and Psychological Measurement, 1978
This note described a FORTRAN IV, CDC, computer program for the canonical analysis of a two-way contingency table. (Author)
Descriptors: Computer Programs, Correlation, Multivariate Analysis, Nonparametric Statistics
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van den Wollenberg, Arnold L. – Psychometrika, 1977
A component method is presented for maximizing estimates of a statistical procedure called redundancy analysis. Relationships of redundancy analysis to multiple correlation and principal component analysis are pointed out. An elaborate example comparing canonical correlation analysis and redundancy analysis on artificial data is presented.…
Descriptors: Correlation, Factor Analysis, Multivariate Analysis, Orthogonal Rotation
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Tinsley, Howard E. A.; Tinsley, Diane J. – Journal of Counseling Psychology, 1987
Explains factor analysis, discussing its relation to other multivariate techniques and describing characteristics of the data to consider in determining the appropriateness of factor analysis. Reviews considerations in making decisions about communality estimates, factor extraction, the number of factors to rotate, methods of factor rotation,…
Descriptors: Behavioral Science Research, Correlation, Counseling, Factor Analysis
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Reddon, John R. – Journal of Educational Statistics, 1987
Computer sampling from a multivariate normal spherical population was used to evaluate Type I error rates for a test of P = I based on Fisher's tanh(sup minus 1) variance stabilizing transformation of the correlation coefficient. (Author/TJH)
Descriptors: Computer Simulation, Correlation, Monte Carlo Methods, Multivariate Analysis
Randolph, Justus J. – Online Submission, 2005
Fleiss' popular multirater kappa is known to be influenced by prevalence and bias, which can lead to the paradox of high agreement but low kappa. It also assumes that raters are restricted in how they can distribute cases across categories, which is not a typical feature of many agreement studies. In this article, a free-marginal, multirater…
Descriptors: Multivariate Analysis, Statistical Distributions, Statistical Bias, Interrater Reliability
Humphries-Wadsworth, Terresa M. – 1998
D. Wood and J. Erskine (1976) and B. Thompson (1989) provided bibliographies of roughly 130 applications of canonical correlation analysis, but the features of such reports have not been widely studied. This report examines the features of recent canonical reports, including substantive inquiries, but also measurement applications examining…
Descriptors: Correlation, Definitions, Literature Reviews, Multivariate Analysis
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Pruzek, Robert M.; Rabinowitz, Stanley N. – American Educational Research Journal, 1981
Simple modifications of principal component methods are described that have distinct advantages for structural analysis of relations among educational and psychological variables. The methods are contrasted theoretically and empirically with conventional principal component methods and with maximum likelihood factor analysis. (Author/GK)
Descriptors: Factor Analysis, Mathematical Models, Maximum Likelihood Statistics, Multivariate Analysis
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DeSarbo, Wayne S. – Psychometrika, 1981
Canonical correlation and redundancy analysis are two approaches to analyzing the interrelationships between two sets of measurements made on the same variables. A component method is presented which uses aspects of both approaches. An empirical example is also presented. (Author/JKS)
Descriptors: Correlation, Data Analysis, Factor Analysis, Mathematical Models
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Katz, Barry M.; McSweeney, Maryellen – Multivariate Behavioral Research, 1980
An explicit statement of a statistic which is a nonparametric analog to one-way MANOVA is presented. The statistic is a multivariate extension of the nonparametric Kruskal-Wallis test (1952). In addition two post hoc procedures are developed and compared. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Multivariate Analysis, Nonparametric Statistics
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Thompson, Bruce; Frankiewicz, Ronald G. – Educational and Psychological Measurement, 1979
An overview of several statistics useful in interpreting variates constructed by using canonical correlation analysis is presented. A computer program which calculates coefficients not typically provided by computer packages is discussed. An illustrative example of the output is provided. (Author/JKS)
Descriptors: Computer Programs, Correlation, Multivariate Analysis, Program Descriptions
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Woodward, J. Arthur; Overall, John E. – Educational and Psychological Measurement, 1976
A convenient, two-stage general linear regression approach to analysis of variance is described for use in univariate or multivariate designs involving one repeated measurement factor and one or more independent classification factors. A brief illustrative example is provided. (Author)
Descriptors: Analysis of Variance, Interaction, Multiple Regression Analysis, Multivariate Analysis
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Wood, Donald A.; Erskine, James A. – Educational and Psychological Measurement, 1976
A review of the development of canonical correlation is presented and insufficiencies in applications from the literature are summarized. A list of analytical procedures for use in canonical correlation is detailed. The relationship between analysis and interpretation is emphasized. An example is presented. (JKS)
Descriptors: Correlation, Multivariate Analysis, Predictor Variables, Research Methodology
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Jedidi, Kamel; And Others – Structural Equation Modeling, 1996
An Expectation-Maximization (EM) algorithm in a maximum likelihood framework is developed to estimate finite mixtures of multivariate regression and simultaneous equation models with multiple endogenous variables. A dataset with cross-sectional observations for a diverse sample of businesses illustrates the semiparametric approach. (SLD)
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Multivariate Analysis, Regression (Statistics)
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Enders, Craig K. – Measurement and Evaluation in Counseling and Development, 2003
This article illustrates 2 follow-up procedures that can be used to examine multivariate analysis of variance (MANOVA) group differences: the univariate analysis of a linear composite variable and multivariate contrasts. A heuristic data set is used to demonstrate the procedures, and it is shown that the follow-up methods will not always yield…
Descriptors: Counseling, Evaluation Methods, Multivariate Analysis, Research Design
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