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Schludermann, Shirin; Schludermann, Edward – J Psychol, 1969
Descriptors: Adults, Factor Analysis, Role Perception, Semantics
Peer reviewed Peer reviewed
Kaiser, Henry F. – Educational and Psychological Measurement, 1981
A revised version of Kaiser's Measure of Sampling Adequacy for factor-analytic data matrices is presented. (Author)
Descriptors: Correlation, Factor Analysis, Research Problems, Sampling
Peer reviewed Peer reviewed
Williams, James S. – Psychometrika, 1981
A revised theorem is presented concerning uniqueness of minimum rank solutions in common factor analysis. (Author)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Kaiser, Henry F.; Cerny, Barbara A. – Educational and Psychological Measurement, 1979
Whether to factor the image correlation matrix or to use a new model with an alpha factor analysis of it is mentioned, with particular reference to the determinacy problem. It is pointed out that the distribution of the images is sensibly multivariate normal, making for "better" factor analyses. (Author/CTM)
Descriptors: Correlation, Factor Analysis, Matrices, Oblique Rotation
Peer reviewed Peer reviewed
Algina, James – Psychometrika, 1980
Conditions for removing the indeterminancy due to rotation are given for both the oblique and orthogonal factor analysis models. The conditions indicate why published counterexamples to conditions discussed by Joreskog are not identifiable. (Author)
Descriptors: Factor Analysis, Oblique Rotation, Orthogonal Rotation
Peer reviewed Peer reviewed
Entin, Eileen B.; Klare, George R. – Journal of Reading Behavior, 1978
Three correlation matrices of readability variables were factor analyzed and the results compared to factor analyses of earlier matrices and to each other. (HOD)
Descriptors: Correlation, Factor Analysis, Readability, Reading Research
Peer reviewed Peer reviewed
Armenakis, Achilles A.; And Others – Educational and Psychological Measurement, 1977
The coefficient of congruence is a quantitative measure of the similarity of factor structures for different samples of subjects. This paper is intended to inform interested readers of the availability of a computer program capable of computing coefficients of congruence of factor structures. (Author)
Descriptors: Computer Programs, Factor Analysis, Hypothesis Testing
Peer reviewed Peer reviewed
Myerberg, N. James – Educational and Psychological Measurement, 1977
A computer program to perform multiple-group factor analysis is presented. Written in Fortran Four, the program has a special feature to handle variables with strong negative correlations. (Author)
Descriptors: Computer Programs, Factor Analysis, Research Design
Peer reviewed Peer reviewed
Gorsuch, Richard L. – Educational and Psychological Measurement, 1997
In exploratory common factor analysis, extension analysis refers to computing the relationship of the common factors to variables that were not included in the factor analysis. A new extension procedure is presented that gives correlations without using estimated factor scores. Advantages of the new method are illustrated. (SLD)
Descriptors: Correlation, Factor Analysis, Research Methodology, Scores
Peer reviewed Peer reviewed
Mulaik, Stanley A.; Quartetti, Douglas A. – Structural Equation Modeling, 1997
The Schmid-Leiman (J. Schmid and J. M. Leiman, 1957) decomposition of a hierarchical factor model converts the model to a constrained case of a bifactor model with orthogonal common factors that is equivalent to the hierarchical model. This article discusses the equivalence of the hierarchical and bifactor models. (Author/SLD)
Descriptors: Factor Analysis, Factor Structure, Mathematical Models
Peer reviewed Peer reviewed
Shapiro, Alexander; ten Berge, Jos M. F. – Psychometrika, 2002
Developed a closed form expression for the asymptotic bias of the explained common variance, or the unexplained common variance under assumptions of multivariate normality in minimum rank factor analysis. Findings from existing data sets show that the presented asymptotic statistical inference is based on a recently developed perturbation theory…
Descriptors: Equations (Mathematics), Factor Analysis, Statistical Inference
Peer reviewed Peer reviewed
Lambert, Zarrel V.; And Others – Educational and Psychological Measurement, 1990
Use of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used factor criteria. These criteria are eigenvalues larger than one and the Scree test. (TJH)
Descriptors: Evaluation Criteria, Factor Analysis, Matrices, Sampling
Peer reviewed Peer reviewed
Levin, Joseph – Multivariate Behavioral Research, 1988
A means of transforming multitrait-multimethod (MTMM) matrices into a classical multiple group factor analysis is outlined. A reanalysis of two numerical illustrations shows that the classical procedure yields results similar to those reached by D. N. Jackson's (1975) two-step procedure for analysis of MTMM matrices. (TJH)
Descriptors: Factor Analysis, Matrices, Multitrait Multimethod Techniques
Peer reviewed Peer reviewed
Carr, Sonya C. – Measurement and Evaluation in Counseling and Development, 1992
Reviews entities that can be factored, with emphasis on Q-technique analyses. Explains basic rationale for Q-technique factor analysis, offers guidelines regarding use of Q-technique factor analysis, presents studies to illustrate applications of Q-technique factor analysis, and discusses special considerations with regard to Q-technique factor…
Descriptors: Data Analysis, Factor Analysis, Q Methodology
Peer reviewed Peer reviewed
Smith, B.; Caputi, P.; Rawstorne, P. – Computers in Human Behavior, 2000
Describes a study that defined and provided initial empirical support for differentiating the concepts of computer attitude, subjective computer experience, and objective computer experience. Discusses results of a principal component factor analysis and presents a conceptual analysis of the relation between subjective computer experience and…
Descriptors: Computer Attitudes, Factor Analysis, Prior Learning
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