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The present paper proposes a strategy for conceptualizing moderating relationships based on their type--strictly correlational and classical correlational--and form--continuous, non-continuous, logistic, and quantum. The results of two computer simulation studies comparing the relative power of three statistical approaches for assessing moderator variables are presented: Moderated multiple regression, multiple regression with a dichotomized moderator, and correlational analysis. As predicted, moderated multiple regression was generally found to be equal or superior to these alternative approaches at detecting moderator relationships. While the alternative approaches did potentially offer greater statistical power under limited circumstances, this was only true in more extreme cases and required fairly accurate estimates of specific characteristics of the joint distribution of the predictor, moderator, and outcome variables.
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