The analytical procedure of path analysis is described in terms of its use in nonexperimental settings in the social sciences. The description assumes a moderate statistical background on the part of the reader. (JKS)

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)

Some of the more simplified methods for contrasts with equal sample sizes in multiple regression analysis are shown to result in erroneous calculations when applied to data sets with unequal sample sizes. An alternative method is provided. (Author/JKS)

This paper discusses how full model dummy variables can be used with partial correlation or multiple regression procedures to compute matrices of pooled within-group correlations. (Author/CTM)

This paper relates common statistics from contingency table analysis to the more familiar R squared terminology in order to better understand the strength of the relation implied. The method of coding contingency tables was shown, as well as how R squared related to phi, V, and chi squared. (Author/CTM)

Recent criticism in the literature of the use of inferential statistics in educational research is refuted. The authors focus on the defense of multiple regression analysis. (JKS)

The construction and interpretation of confidence intervals for the prediction of new cases in multiple regression analysis is explained. An example is provided. (JKS)

Problems associated with the use of gain scores, analysis of covariance, multicollinearity, part and partial correlation, and the lack of rectilinearity in regression are discussed. Particular attention is paid to the misuse of statistical techniques. (JKS)

With even the simplest bivariate regression, least-squares solutions are inappropriate unless one assumes a priori that reciprocal effects are absent, or at least implausible. While this discussion is limited to bivariate regression, the issues apply equally to multivariate regression, including stepwise regression. (Author/CTM)

Of 50 students taking a graduate course in multiple regression analysis at a particular university, they tended to use multiple regression in their dissertations. (Author/JKS)