### The Moderator-Mediator Variable Distinction: A Summary

In The Moderator-Mediator Variable article by Baron and Kenny, 1986, the authors distinguish between these two terms, Moderators and mediators, in a way that helps researchers understanding the ways people behave. First, at the conceptual stage, the authors define the Moderator as: “A third variable, which partitions a focal independent variable into subgroups that establish its domains of maximal effectiveness in regard to a given dependent variable; and (b) The Mediator, “which represents the generative mechanism through which the focal independent variable is able to influence the dependent variable of interest” (p. 1173). If you are confused, fear not; we will unpack these onwards. Second, at the strategic stage, the authors go on to state examples where previous research has mistakenly used these terms interchangeably, noting social psychological studies on social loafing and locus of control on academic achievement. Third, at the statistical level, the authors’ purpose of this article is to focus on differences between the two terms in relation to their use in experimental research designs using correlations as well as Analyses of Variance ANOVAs). So, let’s go!

1) Let’s begin. **A moderator** is “a qualitative (sex, race, class) or quantitative (level of reward) variable that affects the direction and/or strength of the relation between an IV/ Predictor variable and a DV/Criterion variable (the variable being predicted). In a correlation, a Moderator is a third variable that affects the correlation between two variables. Here, the moderator can affect the correlation in either way: strengthen or weaken the correlation. In an ANOVA, the moderator can affect the cross- over interaction of the IV and the various factors that dictate its functions, whether increasing or reducing its occurrence. The authors provide a good example, stating “Glass and Singer’s (1972) finding of an interaction of the factors stressor intensity (noise level) and controllability (periodic-aperiodic noise), … an adverse impact on task performance occurred only when the onset of the noise was aperiodic or unsignaled” (p. 1174). It’s also important that the moderator variable be uncorrelated with the IV (predictor(s) and the DV (criterion). UNLIKE the Mediator, the moderator variables are exogenous to criterion effects (Moderators are always IVs), though mediators can shift their colours like a chameleon and play the parts of Causes and Effects.

2) So, we know that “the causal relation between two variables changes as a function of the moderator variable (p. 1174). So, we must determine the extent of the IV on the DV as a function of a third variable (say it with me, The Moderating Variable). Because correlational analyses are affected by changes in variances, regression models are preferred.

3) Since we know that the moderator variables are exogenous to criterion effects, bias occurs when there exists variability error in the IV across levels of the moderator. The paper provides three examples of how the moderator affects the relation between the IV and the criterion (DV) 1: Linear change between the effect of the IV on the DV as the moderator changes; 2: quadratically (still scratching my head on this example; and 3: a step-function method (more head scratching). Y= X(DV)X(moderator), where X and Z are controlled for to ensure exogeneity. As the authors note, have a read through at Cohen and Cohen (1983) for more clarification.

Let’s continue on with the **Mediator: **(1) A variable is a mediator when it accounts for the relation between the predictor (IV) and the (DV) criterion, or, when certain effects will hold, and they provide clues about “how” or “why” effects occur.

(2) A mediator has the following conditions: (a) variations in levels of the IV significantly account for variations in the presumed mediator; (b) variations in the mediator significantly account for variations in the DV; finally, (c) when relations (a) and (b) are controlled for, a relation between IV and DV that was significant is now zero. If it is not zero, then multiple mediators exist. In social psychological research, reducing that previously significant path to near zero is sufficient.

(3) The authors note that conducting an ANOVA to determine mediators is not ideal; rather, it’s best to first read Fiske, Kenny, and Taylor (1982); secondly, perform three regression equations: (1) regressing the mediator on the IV; (2), regressing the DV on the IV; third, regressing the DV on both the IV and on the mediator. In case (1) the IV must affect the mediator; In case (2), the IV must affect the DV; and in the (3), the mediator must affect the DV. Perfect mediation holds when the IV variable has no effect when the mediator is controlled for.

**Conceptual Distinctions between Moderators and Mediators**

Moderators are introduced when there is a weak correlation between a predictor and a criterion variable. This occurs when one desires to replicate findings in a new setting or with a new sample of participants. Mediators are introduced, on the other hand, when there is a strong relation between the predictor and the criterion, and presumably, you desire to determine the “why” or “how” of the problem in question. What I’m surprised at, however, is that the authors note: “when mediation is at issue, we need to increase both the quality and quantity of the data” (p. 1179). One would presume, however, this would also be a need when a weak correlation exists and multiple moderators are needed to explain low correlation in new settings or in data with new participants. It is also possible to combine mediation with moderation, though the confused student would be wise to page 1180 and James and Brett, 1984, for a more thorough overview of this process.

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