For example, suppose that a business implements a worksite-wellness program (the independent variable, X) to reduce obesity-related health risks in its employees. To investigate these hypotheses, a researcher asks whether the indirect effect is moderated, or whether the mediated effect depends on levels of another variable. To determine the generalizability of these mechanisms or to explain an unexpectedly small mediated effect it may be of interest to investigate whether the mediation relation, or the indirect effect, holds across different subgroups (e.g., men vs. In prevention and intervention research, the mediation model has been used to understand the mechanism(s) by which program effects occur. Is the Process By Which a Program Has an Effect the Same Across Different Types of Participants? Although analyzing mediation and moderation separately for the same data may be useful, as described later in this paper, simultaneous examination of the effects is often relevant and allows for the investigation of more varied, complex research hypotheses. A review of the substantive literature illustrates that few applied research examples have used these models, however. More recent research has presented models to simultaneously estimate mediation and moderation to investigate how the effects work together (e.g., Edwards and Lambert 2007 MacKinnon 2008 Muller et al. Previous research has described the differences between mediation and moderation and has provided methods to analyze them separately (e.g., Dearing and Hamilton 2006 Frazier et al. Investigations of this kind are especially valuable in prevention research where data may present several mediation and moderation relations. The importance of investigating mediation and moderation effects together has been recognized for some time in prevention science, but statistical methods to conduct these analyses are only now being developed. Many of these third variable effects have been investigated in the research literature, and more recent research has examined the influences of more than one third variable effect in an analysis.
Examples of third variables include suppressors, confounders, covariates, mediators, and moderators ( MacKinnon et al. Rather these relations may be modified by, or informed by, the addition of a third variable in the research design. Relations between variables are often more complex than simple bivariate relations between a predictor and a criterion.
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