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Simmen, Martin W. – Multivariate Behavioral Research, 1996
Several methodological issues in the multidimensional scaling of coarse dissimilarities were studied, examining whether it was better to scale dissimilarity data directly or to scale a new matrix derived from the original by row comparisons. Findings support an alternative row-comparison measure based on the Jacard coefficient. (SLD)
Descriptors: Comparative Analysis, Matrices, Multidimensional Scaling, Research Methodology
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Burton, Michael L. – Multivariate Behavioral Research, 1975
Three dissimilarity measures for the unconstrained sorting task are investigated. All three are metrics, but differ in the kind of compensation which they make for differences in the sizes of cells within sortings. Empirical tests of the measures are done with sorting data for occupations names and the names of behaviors, using multidimensional…
Descriptors: Classification, Cluster Analysis, Correlation, Matrices
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Hofmann, Richard J. – Multivariate Behavioral Research, 1975
A generalized matrix procedure is developed for computing the proportionate contribution of a factor, either orthogonal or oblique, to the total common variance of a factor solution. (Author)
Descriptors: Algorithms, Factor Analysis, Matrices, Oblique Rotation
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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.; Horst, Paul – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Error of Measurement, Factor Analysis, Matrices
Peer reviewed Peer reviewed
Steiger, James H. – Multivariate Behavioral Research, 1980
The goodness-of-fit of correlational pattern hypotheses has traditionally been assessed either with a likelihood ratio statistic or with a quadratic form statistic. Several alternative statistics, based on the use of the Fisher r-to-z transform, are proposed and assessed in a Monte Carlo experiment. (Author/JKS)
Descriptors: Correlation, Data Analysis, Hypothesis Testing, Longitudinal Studies
Peer reviewed Peer reviewed
Trendafilov, Nickolay T. – Multivariate Behavioral Research, 1996
An iterative process is proposed for obtaining an orthogonal simple structure solution. At each iteration, a target matrix is constructed such that the relative contributions of the target majorize the original ones, factor by factor. The convergence of the procedure is proven, and the algorithm is illustrated. (SLD)
Descriptors: Algorithms, Factor Analysis, Factor Structure, Matrices
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Tomas, Jose M.; Hontangas, Pedro M.; Oliver, Amparo – Multivariate Behavioral Research, 2000
Assessed two models for confirmatory factor analysis of multitrait-multimethod data through Monte Carlo simulation. The correlated traits-correlated methods (CTCM) and the correlated traits-correlated uniqueness (CTCU) models were compared. Results suggest that CTCU is a good alternative to CTCM in the typical multitrait-multimethod matrix, but…
Descriptors: Matrices, Monte Carlo Methods, Multitrait Multimethod Techniques, Simulation
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ten Berge, Jos M. F. – Multivariate Behavioral Research, 1996
H. F. Kaiser, S. Hunka, and J. Bianchini have presented a method (1971) to compare two matrices of factor loadings based on the same variables, but different groups of individuals. The optimal rotation involved is examined from a mathematical point of view, and the method is shown to be invalid. (SLD)
Descriptors: Comparative Analysis, Factor Structure, Groups, Matrices
Peer reviewed Peer reviewed
Cramer, Elliot M. – Multivariate Behavioral Research, 1974
Descriptors: Correlation, Matrices, Multiple Regression Analysis, Multivariate Analysis
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Linn, Robert L.; And Others – Multivariate Behavioral Research, 1975
Factor structures of student ratings of instruction resulting from total group, between group, and within group analyses were compared. Six factors obtained from responses by students to 31 items were used to approximate the between group covariance matrix based on 437 classroom means and the pooled within classroom covariance matrix. (Author/BJG)
Descriptors: Factor Analysis, Factor Structure, Matrices, Student Evaluation
Peer reviewed Peer reviewed
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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Eiting, Mindert H.; Mellenbergh, Gideon J. – Multivariate Behavioral Research, 1981
A commentary is made on a previously published article concerning testing the equivalence of covariance matrices. An error in the previous article (by the same authors) is pointed out and the consequences of the error are discussed. (JKS)
Descriptors: Analysis of Covariance, Data Analysis, Hypothesis Testing, Matrices
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Reynolds, Thomas J. – Multivariate Behavioral Research, 1980
Order analysis, a technique to isolate unidimensional hierarchies representing multidimensional structure of binary data, is reviewed. Several theoretical flaws inherent in the probalistic version are presented. Suggestions of possible directions for future research are offered. (Author)
Descriptors: Factor Analysis, Item Analysis, Matrices, Statistical Analysis
Peer reviewed Peer reviewed
Reichardt, Charles S.; Coleman, S. C. – Multivariate Behavioral Research, 1995
The criteria for assessing convergent and discriminant validity proposed by D. T. Campbell and D. W. Fiske (1959) are shown to be inadequate for either the additive or multiplicative structures of data in a multitrait-multimethod matrix. Model-specific criteria are more promising for assessing convergent and discriminant validity. (Author/SLD)
Descriptors: Correlation, Criteria, Evaluation Methods, Matrices
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