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Vegelius, Jan – Educational and Psychological Measurement, 1975
The program can compute a great number of different correlation and other statistical measures. The user is free to select among the measures and also among the variables that are read by the program. When a particular set of variables has been treated in the prescribed way, a new set may follow together with new measure definitions. (Author)
Descriptors: Computer Programs, Correlation, Matrices, Statistical Analysis
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Dziuban, Charles D.; Shirkey, Edwin C. – American Educational Research Journal, 1974
Descriptors: Correlation, Factor Analysis, Matrices, Statistical Analysis
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Huck, Schuyler W.; Layne, Benjamin H. – Educational and Psychological Measurement, 1974
Descriptors: Analysis of Variance, Matrices, Statistical Analysis
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Meyer, Edward P. – Educational and Psychological Measurement, 1975
Bounds are obtained for a coefficient proposed by Kaiser as a measure of average correlation and the coefficient is given an interpretation in the context of reliability theory. It is suggested that the root-mean-square intercorrelation may be a more appropriate measure of degree of relationships among a group of variables. (Author)
Descriptors: Correlation, Matrices, Statistical Analysis, Test Reliability
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Shirkey, Edwin C.; Dziuban, Charles D. – Multivariate Behavioral Research, 1976
Distributional characteristics of the measure of sampling adequacy (MSA) were investigated in sample correlation matrices generated from multivariate normal populations with covariance matrix equal to the identity. Systematic variation of sample size and number of variables resulted in minimal fluctuation of the overall MSA from .50. (Author/RC)
Descriptors: Factor Analysis, Matrices, Sampling, Statistical Analysis
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Khatri, C. G.; Rao, C. Radhakrishna – Journal of Multivariate Analysis, 1976
Considers some characterizations of the multivariate normal distribution based on properties of linear functions of dependent vector variables. (RC)
Descriptors: Matrices, Multiple Regression Analysis, Statistical Analysis
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Raju, Nambury S. – Educational and Psychological Measurement, 1983
A direct proof of Tucker, Cooper, and Meredith's procedure for obtaining the squared multiple correlations from a singular correlation matrix is given. (Author)
Descriptors: Correlation, Matrices, Proof (Mathematics), Statistical Analysis
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Joe, George W.; Woodward, J. Arthur – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Matrices, Sampling, Statistical Analysis
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Hakstian, A. Ralph – Educational and Psychological Measurement, 1973
Formulas are presented in this paper for computing scores associated with factors of G, the image covariance matrix, under three conditions. The subject of the paper is restricted to "pure" image analysis. (Author/NE)
Descriptors: Factor Analysis, Matrices, Oblique Rotation, Statistical Analysis
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Hubert, L. J.; Golledge, R. G. – Psychometrika, 1981
A recursive dynamic programing strategy for reorganizing the rows and columns of square proximity matrices is discussed. The strategy is used when the objective function measuring the adequacy of the reorganization has a fairly simple additive structure. (Author/JKS)
Descriptors: Computer Programs, Mathematical Models, Matrices, Statistical Analysis
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Weinberg, Sharon L.; Darlington, Richard B. – Journal of Educational Statistics, 1976
Problems of sampling error and accumulated rounding error in canonical variate analysis are discussed. A new technique is presented which appears to be superior to canonical variate analysis when the ratio of variables to sampling units is greater than one to ten. Examples are presented. (Author/JKS)
Descriptors: Correlation, Matrices, Multivariate Analysis, Sampling
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Hollingsworth, Holly – 1977
A theorem of Spjotvoll (1972) was used to determine and apply a confidence interval for the difference of two multiple correlations based on observations from a single sample. Spjotvoll's method of comparing regression functions is also applicable to a comparison of dependent multiple correlations, an unsolved problem posed by Hotelling in 1940.…
Descriptors: Comparative Analysis, Correlation, Matrices, Predictor Variables
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Humphreys, Lloyd G.; Montanelli, Richard G. – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Factor Analysis, Matrices, Sampling
Peer reviewed Peer reviewed
Kaiser, Henry F.; Michael, William B. – Educational and Psychological Measurement, 1975
An alternative derivation of Tryon's basic formula for the coefficient of domain validity or the coefficient of generalizability developed by Cronbach, Rajaratnam, and Glaser is provided. This derivation, which is also the generalized Kuder-Richardson coefficient, requires a relatively minimal number of assumptions compared with that in previously…
Descriptors: Matrices, Sampling, Statistical Analysis, Test Reliability
Behm, Robert J.; Schill, William J.
A technique for assessing the agreement between the Q-sorts of two or more groups of subjects is presented which relies on the relationship between the Kendall coefficient of concordance (W) and the Spearman rank order correlation (rho). The proposed statistical treatment of Q-sort data involves the use of a number of intercorrelations rather than…
Descriptors: Correlation, Matrices, Q Methodology, Statistical Analysis
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