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Mulaik, Stanley A. – Multivariate Behavioral Research, 1987
Exploratory factor analysis derives its key ideas from many sources, including Aristotle, Francis Bacon, Descartes, Pearson and Yule, and Kant. The conclusions of exploratory factor analysis are never complete without subsequent confirmatory factor analysis. (Author/GDC)
Descriptors: Factor Analysis, History, Induction, Mathematical Models
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Guadagnoli, Edward; Velicer, Wayne – Multivariate Behavioral Research, 1991
In matrix comparison, the performance of four vector matching indices (the coefficient of congruence, the Pearson product moment correlation, the "s"-statistic, and kappa) was evaluated. Advantages and disadvantages of each index are discussed, and the performance of each was assessed within the framework of principal components…
Descriptors: Comparative Analysis, Factor Analysis, Mathematical Models, Matrices
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Snyder, Conrad W., Jr.; Law, Henry G. – Multivariate Behavioral Research, 1979
As psychologists increasingly employ more elaborate and comprehensive data collection schemes, sophisticated analytic techniques will play an ever more important role in understanding behavioral data. This paper outlines one such promising technique, Tucker's three-mode factor analysis, which enables the researcher to explore new taxonomic…
Descriptors: Computer Programs, Factor Analysis, Longitudinal Studies, Mathematical Models
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Hofmann, Richard J. – Multivariate Behavioral Research, 1978
A computational algorithm, called the orthotran solution, is developed for determining oblique factor analytic solutions utilizing orthogonal transformation matrices. Selected results from illustrative studies are provided. (Author/JKS)
Descriptors: Factor Analysis, Mathematical Models, Matrices, Oblique Rotation
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Maraun, Michael D.; And Others – Multivariate Behavioral Research, 1996
The issue of indeterminacy in factor analysis and the debate between the proposed alternative solution and posterior moment position are explored in an article and 14 commentaries and rebuttals in two rounds. Implications for applied work involving factor analysis are discussed. (SLD)
Descriptors: Factor Analysis, Factor Structure, Mathematical Models, Metaphors
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Wood, Phillip – Multivariate Behavioral Research, 1992
Two Statistical Analysis System (SAS) macros are presented that perform the modified principal components approach of L. R. Tucker (1966) to modeling generalized learning curves analysis up to a rotation of the components. Three SAS macros are described that rotate the factor patterns to have characteristics Tucker considered desirable. (SLD)
Descriptors: Algorithms, Change, Computer Software, Factor Analysis
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Krus, David J.; Weiss, David J. – Multivariate Behavioral Research, 1976
Results of empirical comparisons of an inferential model of order analysis with factor analytic models were reported for two sets of data. On the prestructured data set both order and factor analytic models returned its dimensions of length, width and height, but on the random data set the factor analytic models indicated the presence of…
Descriptors: Comparative Analysis, Data Analysis, Factor Analysis, Mathematical Models
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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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Velicer, Wayne F.; Fava, Joseph L. – Multivariate Behavioral Research, 1987
Principal component analysis, image component analysis, and maximum likelihood factor analysis were compared to assess the effects of variable sampling. Results with respect to degree of saturation and average number of variables per factor were clear and dramatic. Differential effects on boundary cases and nonconvergence problems were also found.…
Descriptors: Analysis of Variance, Factor Analysis, Mathematical Models, Matrices
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Levin, Joseph – Multivariate Behavioral Research, 1974
Descriptors: Classification, Correlation, Factor Analysis, Mathematical Models
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Cudeck, Robert – Multivariate Behavioral Research, 1982
Many models have been proposed for examining factors from several batteries of tests. A model for such an analysis is presented which allows for maintaining the distinction among batteries. A discussion of the computational procedures is given, and examples are provided. (Author/JKS)
Descriptors: Correlation, Data Analysis, Factor Analysis, Mathematical Models
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Bentler, Peter M. – Multivariate Behavioral Research, 1976
A general statistical model for the multivariate analysis of mean and covariance structures is described. Matrix calculus is used to develop the statistical aspects of one new special case in detail. This special case separates the confounding of principal components and factor analysis. (DEP)
Descriptors: Analysis of Covariance, Calculus, Comparative Analysis, Factor Analysis
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Fava, Joseph L.; Velicer, Wayne F. – Multivariate Behavioral Research, 1992
Effects of overextracting factors and components within and between maximum likelihood factor analysis and principal components analysis were examined through computer simulation of a range of factor and component patterns. Results demonstrate similarity of component and factor scores during overextraction. Overall, results indicate that…
Descriptors: Computer Simulation, Correlation, Factor Analysis, Mathematical Models
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Undheim, Johan Olav; Gustafsson, Jan-Eric – Multivariate Behavioral Research, 1987
The hypothesis that fluid intelligence is equivalent to the factor of general intelligence is investigated using LISREL to specify higher-order models in reanalyses of three sets of psychometric data from subjects 11, 13, and 15 years old. The three studies showed fluid intelligence to be equivalent with a general factor. (LMO)
Descriptors: Cognitive Ability, Elementary Secondary Education, Factor Analysis, Foreign Countries
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Rindskopf, David; Rose, Tedd – Multivariate Behavioral Research, 1988
Confirmatory factor analysis was applied to test second- and higher-order factor models in the areas of structure of abilities, allometry, and the separation of specific and error variance estimates. The estimation of validity and reliability, second-order models within factor analysis models, and the concept of discriminability were also studied.…
Descriptors: Discriminant Analysis, Error of Measurement, Estimation (Mathematics), Factor Analysis
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