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Peer reviewedNeudecker, H. – Psychometrika, 1981
A full-fledged matrix derivation of Sherin's matrix formulation of Kaiser's varimax criterion is provided. Matrix differential calculus is used in conjunction with the Hadamard (or Schur) matrix product. Two results on Hadamard products are presented. (Author/JKS)
Descriptors: Factor Analysis, Matrices, Orthogonal Rotation
Peer reviewedNevels, Klaas – Psychometrika, 1986
A completing-the-squares type approach to the varimax rotation problem is presented. This approach yields a direct proof of global optimality of a solution for optimal rotation in a plane. (Author/LMO)
Descriptors: Least Squares Statistics, Matrices, Orthogonal Rotation, Statistical Studies
Hakstian, A. Ralph – 1973
Over the years, a number of rationales have been advanced to solve the problem of "blind" oblique factor transformation. By blind transformation is meant the transformation of orthogonal--and often interpretively ineffectual--factors to a position usually dictated by Thurstone's principles of simple structure, but not influenced by a…
Descriptors: Factor Analysis, Mathematical Models, Matrices, Oblique Rotation
Peer reviewedBrokken, Frank B. – Psychometrika, 1985
A generalized congruence maximization procedure for the case of m matrices is presented. The orthogonal rotation procedure simultaneously maximizes the sums of all coefficients of congruence between corresponding factors of m factor matrices. (NSF)
Descriptors: Factor Analysis, Matrices, Orthogonal Rotation, Rating Scales
Peer reviewedten Berge, Jos M. F. – Psychometrika, 1979
Tucker's method of oblique congruence rotation is shown to be equivalent to a procedure by Meredith. This implies that Monte Carlo studies on congruence by Nesselroade, Baltes, and Labouvie and by Korth and Tucker are highly comparable. The problem of rotating two matrices orthogonally to maximal congruence is considered. (Author/CTM)
Descriptors: Factor Analysis, Factor Structure, Matrices, Oblique Rotation
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 reviewedGuertin, Azza S.; And Others – Educational and Psychological Measurement, 1981
The effects of under and overrotation on common factor loading stability under three levels of common variance and three levels or error are examined. Four representative factor matrices were selected. Results suggested that matrices which account for large amounts of common variance tend to have stable factor loadings. (Author/RL)
Descriptors: Analysis of Variance, Correlation, Error of Measurement, Factor Structure
Peer reviewedBrokken, Frank B. – Psychometrika, 1983
Procedures for assessing the invariance of factors across data sets often use the least squares criterion, which appears to be too restrictive. Tucker's coefficient of congruence is proposed as an alternative. A method that maximizes the sum of the coefficients of congruence between two matrices of loadings is presented. (Author/JKS)
Descriptors: Factor Analysis, Factor Structure, Goodness of Fit, Least Squares Statistics
Skakun, Ernest N.; Hakstian, A. Ralph – 1974
Two population raw data matrices were constructed by computer simulation techniques. Each consisted of 10,000 subjects and 12 variables, and each was constructed according to an underlying factorial model consisting of four major common factors, eight minor common factors, and 12 unique factors. The computer simulation techniques were employed to…
Descriptors: Comparative Analysis, Factor Analysis, Least Squares Statistics, Matrices
Hofman, Richard J. – 1975
In this paper 12 blind transformation procedures are applied to 18 data sets. The results of the analyses indicate that the orthotran transformation solution is not restricted to particular types of data as are so many other transformation solutions. The evidence presented in this paper strongly suggests that the orthotran solution must be…
Descriptors: Data Analysis, Factor Analysis, Factor Structure, Matrices
Sauls, Judith M.; Larson, Robert C. – 1975
National data was obtained from 9-year-old, 13-year-old, 17-year-old, and 26 through 35-year-old populations in order to determine academic achievement in nine subject areas. For each age population, group data was calculated and reported by region, sex, color, parents' educational level, and size and type of community. The application of singular…
Descriptors: Academic Achievement, Age Differences, Community Characteristics, Demography
Hall, Charles E.; And Others – 1973
The VARAN (variance Analysis) program is an addition to a series of computer programs for multivariate analysis of variance. The development of VARAN exploits the full linear model. Analysis of variance, univariate and multivariate, is the program's main target. Correlation analysis of all types is available with printout in the vernacular of…
Descriptors: Analysis of Variance, Computer Programs, Correlation, Data Processing
Simon, Charles W. – 1975
An "undesigned" experiment is one in which the predictor variables are correlated, either due to a failure to complete a design or because the investigator was unable to select or control relevant experimental conditions. The traditional method of analyzing this class of experiment--multiple regression analysis based on a least squares…
Descriptors: Bias, Computer Programs, Correlation, Data Analysis


