Due to its extensive applicability and computational ease, moderated multiple regression (MMR) has been widely employed to analyze interaction effects between 2 continuous predictor variables. Accordingly, considerable attention has been drawn toward the supposed multicollinearity problem between predictor variables and their cross-product term.…

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N greater than 16,000 students who were college freshman in 1994-1995 and who were also participants in high-level college athletics. At a second level of…

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

The cross-validated classification accuracies of three predictor weighting strategies (least squares, ridge regression, and reduced rank) were compared under varying simulated data conditions for the two-group classification problem. Results were somewhat similar to previous findings with multiple regression when absolute rather than relative…

This paper considers the case of two predictor variables. Figures are obtained which show the regions of significance of joint regression coefficients, regression coefficients considered separately, and the multiple correlation. The intersection of these regions of significance and non-significance illustrates how the various apparent…

By using complex multiple regression models to generate regression surfaces, the relationships between academic achievement, creativity, and intelligence are examined. Findings indicate that for certain academic subjects creativity is related to achievement up to a threshold level of intelligence, but after the threshold has been reached…

Many important research hypotheses concern conditional relations in which the effect of one predictor varies with the value of another. Such relations are commonly evaluated as multiplicative interactions and can be tested in both fixed-and random-effects regression. Often, these interactive effects must be further probed to fully explicate the…

The effectiveness of the Tabu variable selection algorithm, to identify predictor variables related to a criterion variable, is compared with the stepwise variable selection method and the all possible regression approach. Considering results obtained from previous research, Tabu is more successful in identifying relevant variables than the…