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Peer reviewedZenisek, Thomas J. – Educational and Psychological Measurement, 1978
A FORTRAN computer program was derived for an IBM series 360/370 computer system that provides a factor analytic solution for large three-dimensional data matrices. The computational procedures employed are based upon those presented in Method III by Tucker. (Author/JKS)
Descriptors: Computer Programs, Factor Analysis, Matrices, Multidimensional Scaling
Peer reviewedSamejima, Fumiko – Psychometrika, 1974
Descriptors: Factor Analysis, Latent Trait Theory, Matrices, Models
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 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 reviewedKrus, David J. – Applied Psychological Measurement, 1978
The Cartesian theory of dimensionality (defined in terms of geometric distances between points in the test space) and Leibnitzian theory (defined in terms of order-generative connected, transitive, and asymmetric relations) are contrasted in terms of the difference between a factor analysis and an order analysis of the same data. (Author/CTM)
Descriptors: Factor Analysis, Mathematical Models, Matrices, Multidimensional Scaling
Peer reviewedBart, William M. – Applied Psychological Measurement, 1978
Two sets of five items each from the Law School Admission Test were analyzed by two methods of factor analysis, and by the Krus-Bart ordering theoretic method of multidimensional scaling. The results indicated a conceptual gap between latent trait theoretic procedures and order theoretic procedures. (Author/CTM)
Descriptors: Factor Analysis, Higher Education, Mathematical Models, Matrices
Peer reviewedLevin, Joseph – Educational and Psychological Measurement, 1991
A reanalysis of the intercorrelation matrix from a principal components analysis of the Life Styles Inventory was conducted using a Canadian sample. Using nonmetric multidimensional scaling, analyses show an almost perfect circumplex pattern. Results illustrate the inadequacy of factor analytic procedures for the analysis and representation of a…
Descriptors: Attitude Measures, Correlation, Factor Analysis, Foreign Countries
Peer reviewedRamsay, J. O. – Psychometrika, 1975
Many data analysis problems in psychology may be posed conveniently in terms which place the parameters to be estimated on one side of an equation and an expression in these parameters on the other side. A rule for improving the rate of convergence of the iterative solution of such equations is developed and applied to four problems. (Author/RC)
Descriptors: Computer Programs, Data Analysis, Factor Analysis, Individual Differences
De Ayala, R. J.; Hertzog, Melody A. – 1989
This study was undertaken to compare non-metric multidimensional scaling (MDS) and factor analysis (FA) as means of assessing dimensionality in relation to item response theory (IRT). FA assesses correlation matrices, while MDS performs an analysis of proximity measures. Seven data sets were generated; each differed from the others with respect to…
Descriptors: Comparative Analysis, Error of Measurement, Factor Analysis, Latent Trait Theory
Peer reviewedNoma, Elliot – Journal of the American Society for Information Science, 1984
Argues that co-citation methods combine citing behavior of authors by assuming they share common view of scientific literature which affects assessments of dimensionality and clustering of articles. Co-citation matrices, effects of shared point-of-view assumption, and co-citation compared with bibliographic coupling and centroid scaling are…
Descriptors: Bibliographic Coupling, Citations (References), Cluster Analysis, Cluster Grouping
Peer reviewedPham, Tuan Dinh; Mocks, Joachim – Psychometrika, 1992
Sufficient conditions are derived for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis. The limiting covariance matrix is computed. (Author/SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Factor Analysis, Least Squares Statistics
Cohen, Arie; Farley, Frank H. – 1974
Procedures for analyzing common item effects on interscale structure were reviewed and a study using smallest space analysis of the California Psychological Inventory (CPI) reported. Solutions of three matrices--intercorrelation matrix of the original CPI scales, of reduced scales (with common items removed), and of the number of common…
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Factor Structure
Peer reviewedCarroll, Robert M. – Educational and Psychological Measurement, 1976
Examines the similarity between the coordinates which resulted when correlations were used as similarity measures and the factor loadings obtained by factor analyzing the same correlation matrix. Real data, a set of error free data, and some computer generated data containing deliberately introduced sampling error are analyzed. (RC)
Descriptors: Comparative Analysis, Correlation, Data Analysis, Factor Analysis
Peer reviewedGower, J. C. – Psychometrika, 1975
Concerned with another form of analysis of m sets of matrices, the Procrustes idea is generalized so that all m sets are simultaneously translated, rotated, reflected and scaled so that a goodness of fit criterion is optimised. A computational technique is given, results of which can be summarized in analysis of variance form. (RC)
Descriptors: Analysis of Variance, Data Analysis, Factor Analysis, Goodness of Fit
Reynolds, Thomas J. – 1976
A method of factor extraction specific to a binary matrix, illustrated here as a person-by-item response matrix, is presented. The extraction procedure, termed ERGO, differs from the more commonly implemented dimensionalizing techniques, factor analysis and multidimensional scaling, by taking into consideration item difficulty. Utilized in the…
Descriptors: Discriminant Analysis, Factor Analysis, Item Analysis, Matrices
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