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ten Berge, Jos M. F.; Hofstee, Willem K. B. – Psychometrika, 1999
H. Kaiser (1992) has shown that the sum of coefficients alpha of a set of principal components does not change when the components are transformed by an orthogonal rotation. In this paper, the rotational invariance and the successive alpha-optimality are integrated and generalized in a simultaneous approach. (SLD)
Descriptors: Factor Structure, Orthogonal Rotation, Reliability

Jennrich, Robert I. – Psychometrika, 2001
Identifies a general algorithm for orthogonal rotation and shows that when an algorithm parameter alpha is sufficiently large, the algorithm converges monotonically to a stationary point of the rotation criterion from any starting value. Introduces a modification that does not require a large alpha and discusses the use of this modification as a…
Descriptors: Algorithms, Factor Structure, Orthogonal Rotation

Kaiser, Henry F. – Psychometrika, 1974
An index of factorial simplicity, employing a quartimax transformational criteria, is developed. This index is both for each row separately and for a factor pattern matrix as a whole. The index varies between zero and one. The problem of calibrating the index is discussed. (Author/RC)
Descriptors: Factor Analysis, Factor Structure, Matrices, Orthogonal Rotation

Veldman, Donald J. – Multivariate Behavioral Research, 1974
Descriptors: Factor Analysis, Factor Structure, Orthogonal Rotation, Research Problems

Gorsuch, Richard L. – Educational and Psychological Measurement, 1970
Descriptors: Factor Analysis, Factor Structure, Oblique Rotation, Orthogonal Rotation

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

Lawrence, Frank R.; Hancock, Gregory R. – Educational and Psychological Measurement, 1999
Used simulated data to test the integrity of orthogonal factor solutions when varying sample size, factor pattern/structure coefficient magnitude, method of extraction, number of variables, number of factors, and degree of overextraction. Discusses implications of results with regard to overextraction. (SLD)
Descriptors: Factor Analysis, Factor Structure, Orthogonal Rotation, Sample Size

Golding, Stephen L.; Seidman, Edward – Multivariate Behavioral Research, 1974
A relatively simple technique for assessing the convergence of sets of variables across method domains is presented. The technique, two-step principal components analysis, empirically orthogonalizes each method domain into sets of components, and then analyzes convergence among components across domains. (Author)
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Factor Structure

Skakun, Ernest N.; And Others – Multivariate Behavioral Research, 1976
An empirical sampling distribution of the statistic average trace (E'E) for various orders of A matrices was developed through a Monte Carlo approach. A method is presented which can be used as a guideline in determining whether factor structures obtained from two data sets are congruent. (Author/DEP)
Descriptors: Factor Analysis, Factor Structure, Goodness of Fit, Orthogonal Rotation

Hakstian, A. Ralph – Psychometrika, 1976
Examples are presented in which it is either necessary or desirable to transform two sets of orthogonal axes to simple structure positions by means of the same transformation matrix. A solution is outlined which represents a two-matrix extension of the general "orthomax" orthogonal rotation criterion. (Author/RC)
Descriptors: Factor Analysis, Factor Structure, Matrices, Orthogonal Rotation

Hakstian, A. Ralph; Boyd, William M. – Educational and Psychological Measurement, 1972
Descriptors: Factor Analysis, Factor Structure, Orthogonal Rotation, Statistical Analysis

ten 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
Rennie, Kimberly M. – 1997
Rotation is used in almost all exploratory factor analysis (EFA) studies. There are numerous rotation strategies that can be employed in these various applications. This paper reviews the various rotation choices in EFA studies, including confirmatory rotation, and presents criteria useful in selecting rotation methods in various analytic…
Descriptors: Correlation, Factor Analysis, Factor Structure, Oblique Rotation

Hakstian, A. Ralph – Multivariate Behavioral Research, 1975
Descriptors: Computer Programs, Factor Analysis, Factor Structure, Matrices
Plake, Barbara S.; And Others – 1981
The concurrent validity of the Achievement Anxiety Test (AAT) and its factor structure are investigated to provide empirical evidence about the quality of AAT. State and trait anxiety were measured by State and Trait Anxiety Inventory (STAI), mathematics anxiety was assessed by a 24-item revised version of the 98-item Mathematics Anxiety Rating…
Descriptors: Correlation, Factor Structure, Higher Education, Orthogonal Rotation