Within a correlational analysis framework, a moderator is a third variable that affects the zero-order correlation between two other variable. In the more familiar ANOVA terms, a basic moderator effect can be represented as an interaction between a focal IV and a factor that specifies the appropriate conditions for its operation. In other words, an observed relationship may be different at different levels of a third variable. Moderation refers to the situation where the direction and intensity of an effect of a predictor on a criterion depends on the levels or settings of a third variable. In essence, moderators attenuate or exacerbate the effect.
To test:
- Variables entered into the regression equation in a stepwise and hierarchical fashion.
- Control variables (if any were collected) are entered first into the equation.
- In order to derive main effects of X on Y, regress X onto Y for this step
- Add the interaction terms (X x M) to the analysis. If there is a change in total variance explained from step 3 to step 4 this suggests total moderational impact, and r2 values for each interaction term shows the impact of the moderator for each relationship.
A variable is said to function as a mediator to the extent that it accounts for the relation between the predictor and the criterion (X and Y). Mediators explain how external physical events take on internal psychological significance. Whereas moderator variables specify when certain effects will hold, mediators speak to how or why such effects occur.
For a variable to be considered a mediator, it must pass three tests:
- X correlates with Y
- X correlates with M
- M significant impact on Y when X controlled for
- Effect of X on Y when M controlled for is 0 for full mediation
This is mostly been adapted from Baron and Kenny, 1986
(Adapted from course notes)
(Flashcards and other resources here)