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Gilley, William F.; Uhlig, George E. – Psychology: A Quarterly Journal of Human Behavior, 1985
Evaluated the "Cassel Type-A Personality Assessment (TAP)" for identifying Type-A prone individuals (N=110). Factor scores were computed and analyzed by a discriminant analysis which resulted in an overall accuracy of prediction of 95.5 percent of the original group members. (Author/BL)
Descriptors: Factor Analysis, Personality Assessment, Personality Measures, Personality Traits
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Walberg, Herbert J.; And Others – Journal of Educational Research, 1986
Data from a national sample of 1,955 17-year-olds were used to test a model of educational productivity involving ability, motivation, quantity and quality of instruction, and home and classroom environments. A number of factors were found to be significant predictors of student outcomes. (Author/MT)
Descriptors: Academic Achievement, Factor Analysis, High Schools, Influences
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Eysenck, Sybil B. G.; Dimitriou, E. C. – Social Behavior and Personality, 1984
Administered the Junior Eysenck Personality Questionnaire to boys (N=1117) and girls (N=1199) in Greece for standardization purposes. Results indicated that factor comparisons between England and Greece are reasonably high, strongly suggesting identical factors in both countries. (LLL)
Descriptors: Children, Cross Cultural Studies, Elementary Education, Factor Analysis
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Bentler, P. M.; Tanaka, Jeffrey S. – Psychometrika, 1983
Rubin and Thayer recently presented equations to implement maximum likelihood estimation in factor analysis via the EM algorithm. It is argued here that the advantages of using the EM algorithm remain to be demonstrated. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Maximum Likelihood Statistics, Research Problems
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Rubin, Donald B.; Thayer, Dorothy T. – Psychometrika, 1983
The authors respond to a criticism of their earlier article concerning the use of the EM algorithm in maximum likelihood factor analysis. Also included are the comments made by the reviewers of this article. (JKS)
Descriptors: Algorithms, Estimation (Mathematics), Factor Analysis, Maximum Likelihood Statistics
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Gorman, 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
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Koopman, Raymond F. – Psychometrika, 1976
This note proposes an alternative implementation of the regression method which should be slightly faster than the principal components methods for estimating missing data. (RC)
Descriptors: Comparative Analysis, Data Analysis, Factor Analysis, Multiple Regression Analysis
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Simonton, Dean Keith – Social Behavior and Personality, 1975
The interdisciplinary relationships among 15 kinds of creative achievement were examined over 130 generations of European history. A P-technique factor analysis located three major interdisciplinary clusters: (a) discursive; (b) presentational; and (c) rationalism-mysticism. (Author)
Descriptors: Achievement, Creativity, Creativity Research, European History
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Frane, James W. – Psychometrika, 1976
Several procedures are outlined for replacing missing values in multivariate analyses by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive. (Author)
Descriptors: Correlation, Discriminant Analysis, Factor Analysis, Multiple Regression Analysis
Campbell, Todd C. – 1995
This paper discusses alternatives to R-technique factor analysis that are applicable to counseling and psychotherapy. The traditional R-technique involves correlating columns of a data matrix. O, P, Q, S, and T techniques are discussed with particular emphasis on Q-technique. In Q-technique, people are factored across items or variables with the…
Descriptors: Counseling, Factor Analysis, Q Methodology, Research Methodology
Hardigan, Patrick C.; Sisco, Burton R. – 2000
A factor validity study of the Learning Style Profile (LSP) developed by the National Association of Secondary School Principals was conducted. Developed for use with students in grades 6 through 12, the Profile, which consists of 26 questions representing 24 independent subscales, requires approximately 60 minutes to finish. A random sample of…
Descriptors: Cognitive Style, College Students, Factor Analysis, Factor Structure
Gordon, Sue – 1999
This paper describes a questionnaire for exploring university students' approaches to learning statistics and investigates the approaches of more than 200 psychology students studying statistics. The Approaches to Learning Questionnaire (ALSQ) consists of two scales indicating deep and surface approaches to learning respectively. Three underlying…
Descriptors: College Students, Factor Analysis, Higher Education, Learning
Yen, Shu-Jing; Dayton, C. Mitchell – 1999
The latent structure of the reading test of the International Association for the Evaluation of Educational Achievement (IEA) Reading Literacy study was investigated. The use of latent class modeling for investigating the measurement properties of a large-scale reading assessment database is demonstrated. The study focused on response data from…
Descriptors: Databases, Expository Writing, Factor Analysis, Foreign Countries
Marsh, S. Neil – 2001
This paper explains the meaning and use of three important factor analytic statistics: factor scores, factor structure coefficients, and communality coefficients. For the discussion, 301 observations of junior high school students 11 measured variables from a previous study are analyzed. While factors provide the researcher with general…
Descriptors: Factor Analysis, Factor Structure, Junior High School Students, Junior High Schools
Dawadi, Bhaskar R. – 1999
The robustness of the polytomous Item Response Theory (IRT) model to violations of the unidimensionality assumption was studied. A secondary purpose was to provide guidelines to practitioners to help in deciding whether to use an IRT model to analyze their data. In a simulation study, the unidimensionality assumption was deliberately violated by…
Descriptors: Ability, Estimation (Mathematics), Factor Analysis, Item Response Theory
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