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Showing 76 to 90 of 353 results Save | Export
Lord, Frederic M.; Wingersky, Marilyn S. – 1971
Explicit formulas are derived for the asymptotic sampling variances and covariances of the maximum likelihood estimators for factor-analysis parameters in the special case where there is just one common factor. The effect of the number of variables on these variances and covariances is indicated. A formula is given showing to what extent the usual…
Descriptors: Factor Analysis, Factor Structure, Mathematical Models, Mathematics
Peer reviewed Peer reviewed
Williams, James S. – Psychometrika, 1978
A rigorous definition for a factor analysis model and a complete solution of the factor score indeterminacy problem are presented in this technical paper. The meaning and application of these results are discussed. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Bentler, P. M.; Lee, Sik-Yum – Psychometrika, 1978
A special case of Bloxom's version of Tucker's three mode factor analysis model is developed statistically. A goodness of fit test and an empirical example are presented. (Author/JKS)
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Mathematical Models
Peer reviewed Peer reviewed
Young, Forrest W.; And Others – Psychometrika, 1978
Principal components analysis is generalized to the case where any of the variables under consideration can be nominal, ordinal or interval. Hotelling's original formulation is seen to be a special case of this generalization. (JKS)
Descriptors: Correlation, Factor Analysis, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Harris, Chester W. – Psychometrika, 1978
A simple roof is presented: that the squared multiple correlation of a variable with the remaining variables in the set of variables is a lower bound to the communality of that variable. (Author/JKS)
Descriptors: Correlation, Data Analysis, Factor Analysis, Mathematical Models
Peer reviewed Peer reviewed
Meredith, William – Psychometrika, 1977
A group of factor analytic rotation procedures are developed which yield both hyperplane fittings and oblique Procrustean analyses as special cases. It is generally supposed that these techniques are rather different in approach. Illustrations are presented and discussed. (Author/JKS)
Descriptors: Factor Analysis, Mathematical Models, Matrices, Oblique Rotation
Peer reviewed Peer reviewed
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
Peer reviewed Peer reviewed
Hocevar, Dennis; And Others – Educational and Psychological Measurement, 1987
The purpose of this study was to demonstrate a potential difficulty with relying on null models in LISREL confirmatory factor analytic measurement models and to offer several suggestions for dealing with this concern. Using randomly generated factors in a matrix of 46 aptitude tests, it was shown that this approach is unlikely to reject even…
Descriptors: Aptitude Tests, Factor Analysis, Goodness of Fit, High Schools
Peer reviewed Peer reviewed
Takane, Yoshio; de Leeuw, Jan – Psychometrika, 1987
Equivalence of marginal likelihood of the two-parameter normal ogive model in item response theory and factor analysis of dichotomized variables was formally proved. Ordered and unordered categorical data and paired comparisons data were discussed, and a taxonomy of data for the models was suggested. (Author/GDC)
Descriptors: Classification, Factor Analysis, Latent Trait Theory, Mathematical Models
Peer reviewed Peer reviewed
Muthen, Bengt; And Others – Psychometrika, 1987
A general latent variable model allows for maximum likelihood estimation with missing data. LISREL and LISCOMP programs may be used to carry out this estimation. Simulated data were generated. The proposed Full, Quasi-Likelihood estimator was found to be superior to listwise present quasi-likelihood and pairwise present approaches. (Author/GDC)
Descriptors: Computer Simulation, Computer Software, Factor Analysis, Mathematical Models
Peer reviewed Peer reviewed
Skinner, C. J. – Psychometrika, 1986
The extension of regression estimation and poststratification to factor analysis is considered. These methods may be used either to improve the efficiency of estimation or to adjust for the effects of nonrandom selection. The estimation procedure may be formulated in a LISTREL framework. (Author/LMO)
Descriptors: Estimation (Mathematics), Factor Analysis, Mathematical Models, Matrices
Peer reviewed Peer reviewed
Gorsuch, Richard L. – Educational and Psychological Measurement, 1973
Descriptors: Analysis of Covariance, Factor Analysis, Mathematical Models, Statistical Significance
Peer reviewed Peer reviewed
Harper, Dean – Psychometrika, 1972
A procedure is outlined showing how the axiom of local independence for latent structure models can be weakened. (CK)
Descriptors: Algorithms, Factor Analysis, Factor Structure, Mathematical Applications
Peer reviewed Peer reviewed
Gebhardt, Friedrich – Psychometrika, 1971
Descriptors: Computer Programs, Factor Analysis, Goodness of Fit, Mathematical Models
Peer reviewed Peer reviewed
Sachar, Jane – Journal of Experimental Education, 1980
The partial correlation coefficient is derived analytically under exemplary factor patterns. In these patterns, variables are described as an additive composition of a set of orthogonal factors, including general, common, and specific factors. Viewed in this framework, it is evident that the partial correlation may yield spurious results.…
Descriptors: Correlation, Factor Analysis, Factor Structure, Mathematical Models
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