Relative power of moderated multiple regression and the comparison of subgroup correlation coefficients for detecting moderating effects.

Abstract

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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