Resources to interpret Preacher and Hayes (2004) SPSS Macro for Simple Mediation?

[Jaby]

SPSSX List friends-

Have used the wisdom here for sometime, but am having a tough time
interpreting the following output from Hayes great add-on to SPSS:

VARIABLES IN SIMPLE MEDIATION MODEL
 Y        Com_Eval
 X        SDO
 M        ESJ

DESCRIPTIVES STATISTICS AND PEARSON CORRELATIONS
              Mean       SD   Com_Eval       SDO       ESJ
Com_Eval    2.3000     .9232    1.0000     .3830     .4363
SDO         3.2126     .5155     .3830    1.0000     .4069
ESJ         3.1935     .4679     .4363     .4069    1.0000

SAMPLE SIZE
      166

DIRECT AND TOTAL EFFECTS
             Coeff      s.e.     t        Sig(two)
b(YX)       .6858     .1292    5.3092     .0000
b(MX)       .3693     .0647    5.7050     .0000
b(YM.X)     .6631     .1474    4.4983     .0000
b(YX.M)     .4409     .1338    3.2957     .0012

INDIRECT EFFECT AND SIGNIFICANCE USING NORMAL DISTRIBUTION
           Value      s.e.    LL95CI    UL95CI         Z  Sig(two)
Effect     .2449     .0700     .1077     .3821    3.4993     .0005

To me, this suggests a significant mediation by between SDO by ESJ to
Com_Eval. Am I getting this right? Please confirm and feel free to
illuminate! Or if a simple guide to interpretation for this program is
available-steer me in the right direction!

Gracias-

Jaby

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[Swank, Paul R]

I would say that you have evidence supporting an interpretrattion of mediation but of course, that does not make it so. It depends on assumptions of causality that are hard to verify. But that us certainly one interpretation. b(YX) is the direct effect of X on Y without M in the model. b(MX) is the effect of X on the mediator (M). b(YM.X) is the effect of the mediator on Y with X in the model, and b(YX.M) is the direct effect of x on Y with M in the model. The indirect effect is given by b(MX)*b(YM.X) = b(YX) - b(YX.M).
The (% % confidence interval for the indorect effect does not contain 0 so you have evidence that it could be mediation. Like most models, it is easier to disporve than to prove mediation. You have to assume that X cauess M and Y, M casue Y, and that at least part of the effect of X on Y is through its effect on M. Since b(YX.M) is significant, you only have partial mediation.

Paul R. Swank, Ph.D., Professor
Health Promotions and Behavioral Sciences
School of Public Health
University of Texas Health Science Center Houston
________________________________________
From: SPSSX(r) Discussion [[hidden email]] On Behalf Of Jaby [[hidden email]]
Sent: Wednesday, December 12, 2012 10:26 PM
To: [hidden email]
Subject: Resources to interpret Preacher and Hayes (2004) SPSS Macro for       Simple Mediation?

SPSSX List friends-

Have used the wisdom here for sometime, but am having a tough time
interpreting the following output from Hayes great add-on to SPSS:

VARIABLES IN SIMPLE MEDIATION MODEL
 Y        Com_Eval
 X        SDO
 M        ESJ

DESCRIPTIVES STATISTICS AND PEARSON CORRELATIONS
              Mean       SD   Com_Eval       SDO       ESJ
Com_Eval    2.3000     .9232    1.0000     .3830     .4363
SDO         3.2126     .5155     .3830    1.0000     .4069
ESJ         3.1935     .4679     .4363     .4069    1.0000

SAMPLE SIZE
      166

DIRECT AND TOTAL EFFECTS
             Coeff      s.e.     t        Sig(two)
b(YX)       .6858     .1292    5.3092     .0000
b(MX)       .3693     .0647    5.7050     .0000
b(YM.X)     .6631     .1474    4.4983     .0000
b(YX.M)     .4409     .1338    3.2957     .0012

INDIRECT EFFECT AND SIGNIFICANCE USING NORMAL DISTRIBUTION
           Value      s.e.    LL95CI    UL95CI         Z  Sig(two)
Effect     .2449     .0700     .1077     .3821    3.4993     .0005

To me, this suggests a significant mediation by between SDO by ESJ to
Com_Eval. Am I getting this right? Please confirm and feel free to
illuminate! Or if a simple guide to interpretation for this program is
available-steer me in the right direction!

Gracias-

Jaby

=====================
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