In order to make the parallel analysis criterion for determining the number of factors in factor analysis easy to use, regression equations for predicting the logarithms of the latent roots of random correlation matrices, with squared multiple correlations on the diagonal, are presented. (Author/JKS)

A numerical procedure is outlined for obtaining an interval estimate of the regression of true score or observed score, utilizing only the frequency distribution of observed scores. The procedure assumes that the conditional distribution of observed scores for fixed true scores is binomial. Several illustrations are given. (Author/HG)

The important and sometimes difficult-to-grasp concept of regression suppressor variable effects is explored. An inquiry into the phenomenon of suppressor effects is accomplished via a synthesis of the existing literature and the use of a small heuristic data set to improve the accessibility of the concept. Implications for researchers are also…

Multiple linear regression is used to model the effects of violating statistical assumptions on the likelihood of making a Type I error. This procedure is illustrated for the student's t-test (for independent groups) using data from previous Monte Carlo studies in which the actual alpha levels associated with violations of the normality…

This article illustrates the role multiple linear regression can play in developing prediction equations by providing examples of regression models that could be used in answering questions relative to the importance of a single predictor variable, interactions between predictor variables, and the cross-validation and generalizability of…

Paper proposes a set of techniques for measuring the importance" of each independent variable in a multivariate causal law (i.e., a law showing the combined effect of several independent variables on a single dependent variable). (Authors)

Article applies multiple regression analytic technique to the CIPP model. (Authors/MB)

Setwise regression analysis is a new technique developed to allow a stepwise solution when the interest is in sets of variables rather than in single variables. (CK)

The use of segmented polynomial models is explained. Examples of design matrices of dummy variables are given for the least squares analyses of time series and discontinuity quasi-experimental research designs. Linear combinations of dummy variable vectors appear to provide tests of effects in the two quasi-experimental designs. (Author/BW)

Set correlation is a multivariate generalization of multiple regression/correlation analysis that features the employment of overall measures of association interpretable as proportions of variance and the use of set-partialled sets of variables. The statistical development of the theory and several examples are presented. (Author/JKS)

Redundancy analysis is an attempt to provide nonsymmetric measures of the dependence of one set of variables on another set. This paper attempts to clarify the nature of redundancy analysis and its relationships to canonical correlation and multivariate multiple linear regression. (Author/JKS)