A Monte Carlo simulation assessed the relative power of 2 techniques that are commonly used to test for moderating effects. The authors drew 500 samples from simulation-based populations for each of 81 conditions in a design that varied sample size, the reliabilities of 2 predictor variables (1 of which was the moderator variable), and the magnitude of the moderating effect. They tested the null hypothesis of no interaction effect by using moderated multiple regression (MMR). They then successively polychotomized each sample into 2, 3, 4, 6, and 8 subgroups and tested the equality of the subgroup-based correlation coefficients (SCC). Results showed MMR to be more powerful than the SCC strategy for virtually all of the 81 conditions.

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A field study was conducted to examine the effect of timing of leader opinions on group problemsolving performance. University residence life teams ( N = 19) were asked to solve two typical residence hall problems. Teams were randomly assigned to one of two experimental conditions in which leaders either (a) stated their opinions immediately after the presentation of the problem or (b) withheld their opinions until later in the discussion. As predicted, groups with leaders who delayed stating their opinions generated significantly more alternative solutions to the problems, and these alternatives were rated more feasible and more likely to be adopted.

The results of moderated multiple regression (MMR) are highly affected by the unreliability of the predictor variables (regressors). Errors-in-variables regression (EIVR) may remedy this problem as it corrects for measurement error in the regressors, and thus provides less biased parameter estimates. However, little is known about the properties of the EIVR estimators in the moderator variable context. The present study used simulation methods to compare the moderator variable detection capabilities of MMR and EIVR. Specifically, the study examined the bias and mean squared error of the MMR and EIVR estimates under varying conditions of sample size, reliability of the predictor variables, and intercorrelations among the predictor variables. Findings showed that EIVR estimates are superior to MMR estimates when sample size is high (i.e., at least 250) and the reliabilities of the predictors are high (i.e., rij ≥ .65). However, MMR appears to be the better strategy when reliabilities or sample size are low.

The authors thank Maury Buster for providing valuable feedback on earlier drafts of this paper. We also thank three anonymous reviewers and section Editor James Smither for insight and comments that greatly improved the quality of this paper.Correspondence and requests for reprints should be addressed to Laura E. Baranowski,