spss neda: Testing of Mediation Models in SPSS and SAS

Testing of Mediation Models in SPSS and SAS

Most research focuses on relations between two variables, X and Y, and much has been written about two-variable relations, including conditions under which X can be considered a possible cause of Y. These conditions include randomization of units to values of X and independence of units across and within values of X. Mediation in its simplest form represents the addition of a third variable to this X → Y relation, whereby X causes the mediator, M, and M causes Y, so X → M → Y. Mediation is only one of several relations that may be present when a third variable, Z (using Z to represent the third variable), is included in the analysis of a two-variable system. One possibility is that Z causes both X and Y, so that ignoring Z leads to incorrect inference about the relation of X and Y; this would be an example of a confounding variable. In another situation, Z may be related to X and/or Y, so that information about Z improves prediction of Y by X, but does not substantially alter the relation of X to Y when Z is included in the analysis; this is an example of a covariate. Z may also modify the relation of X to Y such that the relation of X to Y differs at different values of Z; this is an example of a moderator or interaction effect. The distinction between a moderator and mediator has been an ongoing topic of research (Baron & Kenny 1986, Holmbeck 1997, Kraemer et al. 2001). A mediator is a variable that is in a causal sequence between two variables, whereas a moderator is not part of a causal sequence between the two variables. More detailed definitions of these variables in a three-variable system may be found in Robins & Greenland (1992).

The single-mediator model is shown in Figure 1, where the variables X, M, and Y are in rectangles and the arrows represent relations among variables. Figure 1 uses the notation most widely applied in psychology, with a representing the relation of X to M, b representing the relation of M to Y adjusted for X, and c′ the relation of X to Y adjusted for M. The symbols e2 and e3 represent residuals in the M and Y variables, respectively. The equations and coefficients corresponding to Figure 1 are discussed below. For now, note that there is a direct effect relating X to Y and a mediated effect by which X indirectly affects Y through M. Given that most prior mediation research has applied this single-mediator model, this review starts with this model. Limitations and extensions of the model are described in subsequent sections.

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It is not a OEM or tryout version.