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Multivariate Behavioral… | 12 |
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Jackson, Douglas N.; Morf, Martin E. – Multivariate Behavioral Research, 1974
A method is proposed and illustrated for estimating the degree to which a factor rotation to a hypothesized target represents an improvement over rotation to a random target. (Author)
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Matrices

Horn, John L.; Engstrom, Robert – Multivariate Behavioral Research, 1979
Cattell's scree test and Bartlett's chi-square test for the number of factors to be retained from a factor analysis are shown to be based on the same rationale, with the former reflecting subject sampling variability, and the latter reflecting variable sampling variability. (Author/JKS)
Descriptors: Comparative Analysis, Factor Analysis, Hypothesis Testing, Statistical Analysis

Lee, Howard B.; Comrey, Andrew L. – Multivariate Behavioral Research, 1979
The popular factor analytic procedure of putting unities in the main diagonal and retaining all factors with an eigenvalue greater than one is criticized with respect to the number of factors retained and resultant estimates of common variance. Ways of avoiding these difficulties are discussed. (JKS)
Descriptors: Factor Analysis, Hypothesis Testing, Oblique Rotation, Orthogonal Rotation

Acito, Franklin; Anderson, Ronald D. – Multivariate Behavioral Research, 1980
Orthogonal target analysis, a technique employed in confirmatory factor analysis, is investigated via a simulation study. The results indicate that the technique will recover the correct underlying population pattern except under very unfavorable data conditions and that a close fit to a binary target is not necessarily forced. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Hypothesis Testing, Oblique Rotation

Hakstian, A. Ralph; And Others – Multivariate Behavioral Research, 1982
Issues related to the decision of the number of factors to retain in factor analyses are identified. Three widely used decision rules--the Kaiser-Guttman (eigenvalue greater than one), scree, and likelihood ratio tests--are investigated using simulated data. Recommendations for use are made. (Author/JKS)
Descriptors: Algorithms, Data Analysis, Factor Analysis, Factor Structure

Zwick, William R. – Multivariate Behavioral Research, 1982
The performance of four rules for determining the number of components (factors) to retain (Kaiser's eigenvalue greater than one, Cattell's scree, Bartlett's test, and Velicer's Map) was investigated across four systematically varied factors (sample size, number of variables, number of components, and component saturation). (Author/JKS)
Descriptors: Algorithms, Data Analysis, Factor Analysis, Factor Structure

Revelle, William; Rocklin, Thomas – Multivariate Behavioral Research, 1979
A new procedure for determining the optimal number of interpretable factors to extract from a correlation matrix is introduced and compared to more conventional procedures. The new method evaluates the magnitude of the very simple structure index of goodness of fit for factor solutions of increasing rank. (Author/CTM)
Descriptors: Factor Analysis, Goodness of Fit, Hypothesis Testing, Research Design

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

Bentler, Peter M. – Multivariate Behavioral Research, 1976
A general statistical model for the multivariate analysis of mean and covariance structures is described. Matrix calculus is used to develop the statistical aspects of one new special case in detail. This special case separates the confounding of principal components and factor analysis. (DEP)
Descriptors: Analysis of Covariance, Calculus, Comparative Analysis, Factor Analysis

Hyde, Janet S.; And Others – Multivariate Behavioral Research, 1975
Results support Sherman's hypothesis that sex differences in tests of field independence, such as Witkin's Rod-and-Frame Test and Embedded Figures Tests, are artifacts of the well-known sex differences in space perception and are therefore not evidence that females are less analytical than males. (Author/BJG)
Descriptors: Cognitive Ability, Cognitive Processes, Factor Analysis, Higher Education

Ofir, Chezy; And Others – Multivariate Behavioral Research, 1987
Three frequently used response formats are compared via analysis of covariance structures. The cumulative results based on four data sets provided evidence inconsistent with previous research suggesting that these formats are interchangeable. The semantic-differential format is most preferred while in most cases the Stapel format is least…
Descriptors: Analysis of Covariance, Factor Analysis, Hypothesis Testing, Mathematical Models

Marsh, Herbert W. – Multivariate Behavioral Research, 1985
This study examines the factor structure of response to the masculinity-femininity (MF) scale of the Comrey Personality Scales for males and females. The use of confirmatory factor analysis for testing hierarchical factor structures and factorial invariance is illustrated. The findings argue that MF is a multifaceted, hierarchical construct.…
Descriptors: Cluster Analysis, Factor Analysis, Factor Structure, Females