Marsh, H. W, Hau, K., Wen, Z., Nagengast, B. & Morin, AJ. (2013). Moderation. T. D. Little. The Oxford handbook of quantitative methods: Vol. 2: Statistical Analysis 361-386. United States of America: Oxford University Press. Retrieved from https://doi.org/10.1093/oxfordhb/9780199934898.013.0017
Moderation (or interaction) occurs when the strength or direction of the effect of a predictor variable on an outcome variable varies as a function of the values of another variable, called a moderator. Moderation effects address critical questions, such as under what circumstances, or for what sort of individuals, does an intervention have a stronger or weaker effect? Moderation can have important theoretical, substantive, and policy implications. Especially in psychology with its emphasis on individual differences, many theoretical models explicitly posit interaction effects. Nevertheless, particularly in applied research, even interactions hypothesized on the basis of strong theory and good intuition are typically small, nonsignificant, or not easily replicated. Part of the problem is that applied researchers often do not know how to test interaction effects, as statistical best practice is still evolving and often not followed. Also, tests of interactions frequently lack power so that meaningfully large interaction effects are not statistically significant. In this chapter we provide an intuitive overview to the issues involved, recent developments in how best to test for interactions, and some directions that further research is likely to take.
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