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Peer reviewedCudeck, Robert – Multivariate Behavioral Research, 1982
Many models have been proposed for examining factors from several batteries of tests. A model for such an analysis is presented which allows for maintaining the distinction among batteries. A discussion of the computational procedures is given, and examples are provided. (Author/JKS)
Descriptors: Correlation, Data Analysis, Factor Analysis, Mathematical Models
Peer reviewedRachman, D.; And Others – Educational and Psychological Measurement, 1981
A confirmatory factor analysis provided support for the result that Holland's Self-Directed Search measures six factors: realistic, investigative, artistic, social-enterprising, conventional, and a sixth general interest factor. Generally, the psychological relationship among types confirms the hexagon model proposed by Holland and others.…
Descriptors: Accountants, Factor Analysis, Factor Structure, Interest Inventories
Peer reviewedJohnstone, J. N.; O'Mara, Deborah A. – Studies in Educational Evaluation, 1981
Educational changes included: (1) expansion of preschool facilities; (2) increased involvement of students in secondary education and in tertiary education; and (3) marked fluctuation in both the economic status of teachers and financial involvement of the aggregate of six state governments in education. (RL)
Descriptors: Educational Assessment, Educational Change, Factor Analysis, Factor Structure
Peer reviewedGuttman, Louis – Perceptual and Motor Skills, 1982
Mathematical and statistical relationships between factor analysis and smallest space analysis are discussed. As spatial analysis of correlation matrices, factor analysis is a special case of smallest space analysis. The two differ in six ways: Shepard diagram, dimensionality, correction for communality, similarity coefficients, regions versus…
Descriptors: Factor Analysis, Factor Structure, Item Analysis, Research Methodology
Peer reviewedHakstian, 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
Peer reviewedZwick, 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
Peer reviewedBadian, Nathlie A. – Journal of Special Education, 1982
All preschool children (N=180) of the same age group in a small town were tested six months before kindergarten entry with the Holbrook Screening Battery which correctly identified 92 percent of Ss scoring as both good and poor readers four years later on the Stanford Achievement Test. (Author/CL)
Descriptors: Age Differences, Factor Analysis, Language Acquisition, Prediction
Peer reviewedThompson, Bruce; Pitts, Murray C. – Journal of Experimental Education, 1981
Adequacy coefficients can be derived by calculating the cosines of the angles between factors' actual and theoretically expected locations within factor space. The use of adequacy coefficients during instrument development is discussed. The development of an instrument to measure thinking interests provides a heuristic framework for the…
Descriptors: Cognitive Style, Factor Analysis, Mathematical Models, Test Construction
Peer reviewedMcDonald, Roderick P. – Psychometrika, 1981
An expression is given for weighted least squares estimators of oblique common factors of factor analyses, constrained to have the same covariance matrix as the factors they estimate. A proof of the uniqueness of the solution is given. (Author/JKS)
Descriptors: Analysis of Covariance, Factor Analysis, Least Squares Statistics, Mathematical Models
Peer reviewedRevelle, 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
Peer reviewedMuthen, Bengt – Psychometrika, 1978
A new method--similar to Christoffersson's--for the factor analysis of dichotomous variables uses information from the first and second order proportions to fit a multiple factor model. The estimation is considerably simplified. Items from Rotter's locus of control inventory are included. (Author/JKS)
Descriptors: Correlation, Factor Analysis, Item Analysis, Personality Measures
Peer reviewedNewman, Isadore; Fraas, John – Multiple Linear Regression Viewpoints, 1979
Issues in the application of multiple regression analysis as a data analytic tool are discussed at some length. Included are discussions on component regression, factor regression, ridge regression, and systems of equations. (JKS)
Descriptors: Correlation, Factor Analysis, Multiple Regression Analysis, Research Design
Peer reviewedPierce, Robert C. – Journal of Leisure Research, 1980
Studies were conducted to determine personal satisfactions individuals obtained from leisure activities that were not obtained from work activities. Results of samples indicated that the four primary motivations were intimacy, relaxation, achievement, and power. Additional satisfactions included novelty, intellectual enjoyment, sociability, and…
Descriptors: Factor Analysis, Job Satisfaction, Leisure Time, Motivation
Peer reviewedPierce, Robert C. – Journal of Leisure Research, 1980
Studies were conducted to rate the aptness of favorite leisure and work activities to the characteristics of individuals participating in these activities. Ratings were cluster analyzed and the results compared. Four dimensions which appeared in both work and leisure activities were destructiveness, cerebration, fulfillment, and flamboyance. (JN)
Descriptors: Cluster Analysis, Factor Analysis, Leisure Time, Personality Traits
Peer reviewedSternberg, Robert J. – Journal of Educational Psychology, 1981
The decline of the psychometric paradigm for studying intelligence was due in part to its failure to meet four challenges. On the surface, users of the information-processing paradigms seem successfully to have met these challenges, but at a deeper level, the level of success is not so great. (Author/BW)
Descriptors: Cognitive Measurement, Cognitive Processes, Comparative Analysis, Correlation


