Illustrates the use of multiple regression analysis for computing conditional probabilities of occurrence of events based on the functional relationship between a dependent response variate and one or more independent predictors. (RC)

An analysis of variance procedure for testing differences in r-squared, the coefficient of determination, across independent samples is proposed and briefly discussed. The principal advantage of the procedure is to minimize Type I error for follow-up tests of pairwise differences. (Author/JKS)

The recognition that all parametric methods are interrelated, coupled with the notion that structure coefficients are often vital in factor and canonical analyses, suggests that structure coefficients may be important in univariate analysis as well. Using a small, heuristic data set, this paper discusses the importance of structure coefficients…

A procedure for rapid screening of variables as potential moderators is presented and discussed. A moderator is defined as any variable which can be used to identify differentially predictable persons; or defined statistically by stating that if a predictor and a moderator are each divided into three or more categories and used as independent…

The value of variance is emphasized, and the element of design, frequently not adequately understood, is clarified to underscore the importance of variance to the researcher. Two analytic methods, analysis of variance (ANOVA) and multiple regression, are discussed in terms of how each uses/applies variance. Advantages and major difficulties with…

Four computer programs using the general purpose multiple linear regression program have been developed. Setwise regression analysis is a stepwise procedure for sets of variables; there will be as many steps as there are sets. Covarmlt allows a solution to the analysis of covariance design with multiple covariates. A third program has three…

Offers a brief explication of commonality analysis; a step-by-step discussion of how communication researchers may perform commonality analyses using output from a computer-assisted statistical analysis program; and provides an extended example illustrating a commonality analysis. (JMF)

A dichotomous dependent variable is used to determine a combination of variables that will predict group membership. Dichotomous variables are frequently encountered in multiple regression analysis. However, several textbooks question the appropriateness of using multiple regression analysis when analyzing dichotomous dependent variables. The…

Develops a testing procedure based on a model including treatment effects on residual variances. (Author/RC)

The purposes of this paper are to provide an explanation of the way in which suppressor variables operate in multiple regression, and to provide illustrations of their effects with actual social science data. Examples are reviewed in which one predictor accounts for more of the variance in the dependent variable when another predictor is included,…

The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…

The authors state that multiple regression is a powerful method of statistical analysis, provides a strength of relationship index, and should replace analysis of variance (ANOVA) in educational research. They also discuss the coding of categorical variables and available computer programs for multiple regression. (Author/MH)

A general procedure is presented for generating code values for a qualitative variable in multiple linear regression analyses that result in directly interpretable estimates of interest. The basic approach, in viewing ANOVA as a multiple regression problem, is to derive quantitative code values for the various levels of the qualitative ANOVA…

Differences in ratings of the same instructor teaching the same course on two different occasions are related to differences in background characteristics: (1) higher levels of workload/difficulty, (2) higher expected grades, and (3) instructor experience. (PN)