The first eigenvalue of a correlation matrix indicates the maximum amount of the variance of the variables which can be accounted for with a linear model by a single underlying factor. The first eigenvalue measures the primary cluster in the matrix, its number of variables and average correlation. (Author/RL)

The proposed method has the advantage of being a rational procedure which reduces the larger set of variables'' down to a desired subset of predictor variables. The selected subset, then, can be coded for a full regression run if it contains multiple level category variables among those selected. (Author)

Following Conger's revised definition of suppressor variables, the universe relationships among two predictors and a criterion is analyzed. A simple mapping of relationships, based on the correlation between two predictors and the ratio of their validities, is provided. The relation between suppressor and part correlation is also discussed.…

The form of the Johnson-Neyman region of significance is shown to be determined by the statistic for testing the null hypothesis that the population within-group regressions are parallel. Results are obtained for both simultaneous and nonsimultaneous regions of significance. (Author)

A generalized measure of association and an associated test of significance are presented for nominal independent variables in which any number or combination of interval, ordinal, or nominal dependent variables can be analyzed. A permutation test of significance is provided for the new measure. (SLD)

A definition of a suppressor variable is presented which is based on the relation of the semipartial correlation to the zero order correlation. Advantages of the definition are given. (Author/JKS)

Major objective in this study was the structuring of a predictive model that would assess combinations of variables that most effectively and parsimoniously measure and forecast college success. (Author)

Study examined models for improving prediction of single and multiple criteria. (Author)

To determine if the observed correlation between two variables can be "explained" by a third variable, a significance test on the partial correlation coefficient is often used. This can be misleading when the third variable is measured with error. This article shows how the problem can be partially overcome. (Author/BW)

Effects of violating the independence assumption when combining correlation coefficients in a meta-analysis were studied. This Monte-Carlo simulation varied sample size, predictor number, population intercorrelation among predictors, and population correlation between predictors and criterion. Combining statistics from nonindependent data in a…

The inter-battery factor analysis model for predictive systems appears to offer a useful formalization of these systems and provides some guides for the development of the system. (Author)

The construction of artificial profiles, or stimuli, for judgment analysis (JAN) is discussed. JAN is a technique for identifying and analyzing the policies of a group of judges for rating the profiles of personnel or applicants. Conditions under which artificial profiles are successfully used are discussed. (SLD)

The effects of sample size, number of variables, and population value of the congruence coefficient on the sampling distribution of the congruence coefficient were examined. Sample data were generated on the basis of the common factor model, and principal axes factor analyses were performed. (Author/LMO)

The economic utility of the following 5 weighting methods for evaluating consumer loan applications was determined using a sample of 443 loans: (1) unit; (2) weighted application blank; (3) chi square; (4) Bayes; and (5) regression. The unit and weighted application blank procedures were the best approaches. (SLD)