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

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

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 – Multivariate Behavioral Research, 1975
Descriptors: Computer Programs, Factor Analysis, Factor Structure, Matrices

Overall, John E. – Multivariate Behavioral Research, 1974
Described is a method for obtaining an oblique simple structure in which primary axes are principal axes of homogeneous subsets of test variables. Examples of its application in R and Q-type analyses are presented. (Author)
Descriptors: Cluster Analysis, Factor Analysis, Factor Structure, Hypothesis Testing