Behavior Research Methods, Instruments, & Computers

Behavior Research Methods, Instruments, & Computers 2004, 36 (4), 717-731

SPSS and SAS procedures for estimating indirect effects in simple mediation models
KRISTOPHER J. PREACHER University of North Carolina, Chapel Hill, North Carolina and ANDREW F. HAYES Ohio State University, Columbus, Ohio Researchers often conduct mediation analysis in order to indirectly assess the effect of a proposedcause on some outcome through a proposed mediator. The utility of mediation analysis stems from its ability to go beyond the merely descriptive to a more functional understanding of the relationships among variables. A necessary component of mediation is a statistically and practically significant indirect effect. Although mediation hypotheses are frequently explored in psychological research,formal significance tests of indirect effects are rarely conducted. After a brief overview of mediation, we argue the importance of directly testing the significance of indirect effects and provide SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals, as well as the traditional approachadvocated by Baron and Kenny (1986). We hope that this discussion and the macros will enhance the frequency of formal mediation tests in the psychology literature. Electronic copies of these macros may be downloaded from the Psychonomic Society’s Web archive at www.psychonomic.org/archive/.

Psychologists often conduct research to establish whether and to what extent one variable affects another.However, the discovery that two variables are related to each other is only one small part of the aim of psychology. Deeper understanding is gained when we comprehend the process that produces the effect. For example, it might be useful to know whether a management training program leads to an increase in employee satisfaction by affecting employee attitudes toward management or by changing behavioralhabits. In this example, attitudes and habits are potential mediators of the relationship between the management training program and employee satisfaction. A variable may be called a mediator “to the extent that it accounts for the relation between the predictor and the criterion” (Baron & Kenny, 1986, p. 1176).1 Panel A of Figure 1 represents the effect of some proposed cause (X ) on some outcome(Y ). Panel B of Figure 1 represents the simplest form of mediation—the type that occurs when one variable (M) mediates the effect of X on Y. We term this model simple mediation. More complex mediation models are possible, but we limit our discussion here to simple mediation because it is by far the most commonly employed type of mediation model.

The simple relationship between X and Y isoften referred to as the total effect of X on Y (see Figure 1, panel A); we denote the total effect c to distinguish it from c¢, the direct effect of X on Y after controlling for M (see Figure 1, panel B). The formal heuristic analysis often used to detect simple mediation effects is straightforward and follows directly from the definition of a mediator provided by Baron and Kenny (1986). Variable Mis considered a mediator if (1) X significantly predicts Y (i.e., c 0 in Figure 1), (2) X significantly predicts M (i.e., a 0 in Figure 1), and (3) M significantly predicts Y controlling for X (i.e., b 0 in Figure 1). Baron and Kenny discuss several analyses that should be performed and the results assessed with respect to the criteria just described. These criteria are assessed by estimating thefollowing equations: ˆ Y = i1 + cX ˆ M = i + aX
2

(1) (1) (2) (3)

ˆ Y = i3 + c ¢X + bM

We thank Nancy Briggs, Donna Coffman, Jinyan Fan, and Robert MacCallum for helpful comments. Correspondence regarding this article should be addressed to K. J. Preacher, Department of Psychology, CB #3270 Davie Hall, University of North Carolina, Chapel Hill, NC 27599-3270 (e-mail: preacher@unc.edu)....