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Peer reviewedKorth, Bruce; Tucker, Ledyard R. – Psychometrika, 1975
Provides normative data about the distribution of one measure of similarity of factor loadings, the congruence coefficient, through a Monte Carlo Technique. Matching of "chance" factor patterns was done by the method of Tucker. Statistical tests of the results, based on similarities of the method to canonical and multiple correlation,…
Descriptors: Correlation, Factor Analysis, Factor Structure, Matrices
Peer reviewedStewart, Thomas R. – Multivariate Behavioral Research, 1974
Suggests a way of using factor analytic techniques to supplement multidimensional scaling in such a way as to provide a firm basis for evaluating multidimensional representations. (Author/RC)
Descriptors: Evaluation Criteria, Factor Analysis, Matrices, Multidimensional Scaling
Peer reviewedLevin, Joseph – Multivariate Behavioral Research, 1974
Descriptors: Classification, Correlation, Factor Analysis, Mathematical Models
Peer reviewedKatz, Jeffrey Owen; Rohlf, F. James – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Comparative Analysis, Correlation, Factor Analysis
Browne, Michael W. – 1973
Gradient methods are employed in orthogonal oblique analytic rotation. Constraints are imposed on the elements of the transformation matrix by means of reparameterisations. (Author)
Descriptors: Algorithms, Factor Analysis, Matrices, Oblique Rotation
Peer reviewedLee, Howard B.; Comrey, Andrew L. – Multivariate Behavioral Research, 1978
Two proposed methods of factor analyzing a correlation matrix using only the off-diagonal elements are compared. The purpose of these methods is to avoid using the diagonal communality elements which are generally unknown and must be estimated. (Author/JKS)
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Matrices
Peer reviewedSpence, Ian; Young, Forrest W. – Psychometrika, 1978
Several nonmetric multidimensional scaling random ranking studies are discussed in response to the preceding article (TM 503 490). The choice of a starting configuration is discussed and the use of principal component analysis in obtaining such a configuration is recommended over a randomly chosen one. (JKS)
Descriptors: Correlation, Factor Analysis, Goodness of Fit, Matrices
Peer reviewedMcDonald, Roderick P. – Psychometrika, 1978
The relationship between the factor structure of a convariance matrix and the factor structure of a partial convariance matrix when one or more variables are partialled out of the original matrix is given in this brief note. (JKS)
Descriptors: Analysis of Covariance, Correlation, Factor Analysis, Factor Structure
Peer reviewedSchurr, K. Terry; Henriksen, L. W. – Educational and Psychological Measurement, 1984
Provided is a description of three methods for testing certain types of a priori hypotheses about differences among covariance matrices. Briefly outlined are procedures for using two computer programs, COFAMM and LISREL, for testing such hypotheses. Also provided are examples of application of the methods to a meaningful data set. (Author/BW)
Descriptors: Analysis of Covariance, Computer Software, Factor Analysis, Hypothesis Testing
Peer reviewedGorman, Bernard S.; Primavera, Louis H. – Journal of Experimental Education, 1983
Factor and cluster analyses are distinctly different multivariate procedures with different goals. However, when used in a complementary fashion, each set of methods can be used to enhance the interpretation of results found in the other set of methods. Simple examples illustrating the joint use of the methods are provided. (Author)
Descriptors: Cluster Analysis, Correlation, Data Analysis, Factor Analysis
Peer reviewedSawyer, Robert N.; And Others – Educational and Psychological Measurement, 1979
High intercorrelations among the six subtests of the Pictorial Test of Intelligence raise questions regarding the construct validity of the instrument. A factor analysis was performed. A single factor with stable factor loadings emerged. (Author/JKS)
Descriptors: Correlation, Factor Analysis, Intelligence Tests, Matrices
Peer reviewedWothke, Werner; Browne, Michael W. – Psychometrika, 1990
The composite direct-product model for the multitrait-multimethod (MTMM) matrix is reparamaterized as a second-order factor analysis model. This process facilitates the use of widely available computer programs--LISREL and LISCOMP--for fitting the model. If bounds on phi and theta are not imposed to avoid inadmissible values, convergence problems…
Descriptors: Factor Analysis, Goodness of Fit, Mathematical Models, Matrices
Peer reviewedFrederiksen, Carl H. – Psychometrika, 1974
Descriptors: Analysis of Covariance, Computer Programs, Factor Analysis, Factor Structure
Phillips, Gary W. – 1982
The usefulness of path analysis as a means of better understanding various linear models is demonstrated. First, two linear models are presented in matrix form using linear structural relations (LISREL) notation. The two models, regression and factor analysis, are shown to be identical although the research question and data matrix to which these…
Descriptors: Estimation (Mathematics), Factor Analysis, Mathematical Models, Matrices
Peer reviewedHakstian, A. Ralph; Abell, Robert A. – Psychometrika, 1974
Four prominent oblique transformation techniques--promax, the Harris-Kaiser procedure, biquartimin, and direct oblimin--are examined and compared. Additionally, two newly developed procedures are presented and included in the comparisons. (Author/RC)
Descriptors: Comparative Analysis, Factor Analysis, Matrices, Measurement Techniques


