Evaluating mediation and moderation effects in school psychology: a presentation of methods and review of current practice

Abstract

Third variable effects elucidate the relation between two other variables, and can describe why they are related or under what conditions they are related. This article demonstrates methods to analyze two third-variable effects: moderation and mediation. The utility of examining moderation and mediation effects in school psychology is described and current use of the analyses in applied school psychology research is reviewed and evaluated. Proper statistical methods to test the effects are presented, and different effect size measures for the models are provided. Extensions of the basic moderator and mediator models are also described.

Fig. 1

Conceptual path diagram of the mediation reading program example.

Fig. 2

Conceptual path diagram of the moderation reading program example.

Fig. 3

Frequency histogram of moderation and mediation publications in selected school psychology journals over time.

Fig. 4

Differentiating interaction effects. The solid line in each graph across the panels pertains to the X–Y relation at one level of the moderator variable (i.e., learning disabled). The dashed line in each graph across the panels pertains to the X–Y relation at the other level of the moderator variable (i.e., not learning disabled).

Fig. 5

The basic moderation model. X=the independent variable, Y=the dependent variable, Z=the moderator variable, XZ=the interaction of the independent and moderator variables, ${\widehat{\beta }}_1$ = the effect of the independent variable on the outcome controlling for Z and XZ, ${\widehat{\beta }}_2$ = the effect of the moderator on the outcome controlling for X and XZ, and ${\widehat{\beta }}_3$ = the effect of the XZ interaction on the outcome controlling for the lower order effects. *Corresponds to p<.05 and ns corresponds to not significant. Correlations among the predictor variables are approximately zero because of the balanced design in the example.

Fig. 6

Plot of the interaction for the numerical reading program example.

Fig. 7

The basic mediation model. X=the independent variable, Y=the dependent variable, M=the mediator variable, ${\widehat{\beta }}_{1M}$=the effect of the independent variable on the mediator, ${\widehat{\beta }}_{2Y}$=the effect of the mediator on the outcome controlling for X, and ${\widehat{\beta }}_{1Y}$=the direct effect of the X on Y controlling for M. *Corresponds to p<.05, **corresponds to p<.001 and ns corresponds to not significant.