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Peer reviewedCarter, David S. – Educational and Psychological Measurement, 1979
There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)
Descriptors: Comparative Analysis, Correlation, Mathematical Formulas, Multiple Regression Analysis
Peer reviewedLee, S. Y.; Jennrich, R. I. – Psychometrika, 1979
A variety of algorithms for analyzing covariance structures are considered. Additionally, two methods of estimation, maximum likelihood, and weighted least squares are considered. Comparisons are made between these algorithms and factor analysis. (Author/JKS)
Descriptors: Analysis of Covariance, Comparative Analysis, Correlation, Factor Analysis
Peer reviewedLittle, Roderick J. A.; Pullum, Thomas W. – Sociological Methods and Research, 1979
Two methods of analyzing nonorthogonal (uneven cell sizes) cross-classified data sets are compared. The methods are direct standardization and the general linear model. The authors illustrate when direct standardization may be a desirable method of analysis. (JKS)
Descriptors: Analysis of Variance, Comparative Analysis, Mathematical Models, Multiple Regression Analysis
