Mplus code for the mediation, moderation, and moderated mediation model templates from Andrew Hayes'
PROCESS analysis examples
Model 5: 1 or more mediators, in parallel if multiple, 1 moderator of direct IVDV path only
Example Variables: 1 predictor X, 1 mediator M, 1 moderator W, 1 outcome Y
Preliminary notes:
The code below assumes that
 The primary IV (variable X) is continuous or dichotomous
 Any moderators (variables W, V, Q, Z) are continuous, though the only adaptation required to handle dichotomous moderators is in the MODEL CONSTRAINT: and loop plot code  an example of how to do this is given in model 1b. Handling categorical moderators with > 2 categories is demonstrated in
model 1d.
 Any mediators (variable M, or M1, M2, etc.) are continuous and satisfy the assumptions of standard multiple regression. An example of how to handle a dichotomous mediator is given in model 4c.
 The DV (variable Y) is continuous and satisfies the assumptions of standard multiple regression. An example of how to handle a dichotomous DV is given in model 1e (i.e. a moderated logistic regression) and in model 4d (i.e. an indirect effect in a logistic regression).
Model Diagram:
Statistical Diagram:
Model Equation(s):
Y = b0 + b1M + c1'X + c2'W + c3'XW
M = a0 + a1X
Algebra to calculate indirect and/or conditional effects by writing model as Y = a + bX:
Y = b0 + b1M + c1'X + c2'W + c3'XW
M = a0 + a1X
Hence... substituting in equation for M
Y = b0 + b1(a0 + a1X) + c1'X + c2'W + c3'XW
Hence... multiplying out brackets
Y = b0 + a0b1 + a1b1X + c1'X + c2'W + c3'XW
Hence... grouping terms into form Y = a + bX
Y = (b0 + a0b1 + c2'W) + (a1b1 + c1' + c3'W)X
Hence...
One indirect effect of X on Y:
a1b1
One direct effect of X on Y, conditional on W:
c1' + c3'W
Mplus code for the model:
! Predictor variable  X
! Mediator variable(s) – M
! Moderator variable(s)  W
! Outcome variable  Y
USEVARIABLES = X M W Y XW;
! Create interaction term
! Note that it has to be placed at end of USEVARIABLES subcommand above
DEFINE:
XW = X*W;
ANALYSIS:
TYPE = GENERAL;
ESTIMATOR = ML;
BOOTSTRAP = 10000;
! In model statement name each path and intercept using parentheses
MODEL:
[Y] (b0);
Y ON M (b1);
Y ON X (cdash1);
Y ON W (cdash2);
Y ON XW (cdash3);
[M] (a0);
M ON X (a1);
! Use model constraint subcommand to test simple slopes
! You need to pick low, medium and high moderator values,
! for example, of 1 SD below mean, mean, 1 SD above mean
! Also calc total effects at lo, med, hi values of moderator
MODEL CONSTRAINT:
NEW(LOW_W MED_W HIGH_W a1b1 DIR_LO DIR_MED DIR_HI TOT_LO TOT_MED TOT_HI);
LOW_W = #LOWW; ! replace #LOWW in the code with your chosen low value of W
MED_W = #MEDW; ! replace #MEDW in the code with your chosen medium value of W
HIGH_W = #HIGHW; ! replace #HIGHW in the code with your chosen high value of W
! Now calc indirect effect  and conditional direct effects for each value of W
a1b1 = a1*b1;
DIR_LO = cdash1 + cdash3*LOW_W;
DIR_MED = cdash1 + cdash3*MED_W;
DIR_HI = cdash1 + cdash3*HIGH_W;
TOT_LO = DIR_LO + a1b1;
TOT_MED = DIR_MED + a1b1;
TOT_HI = DIR_HI + a1b1;
! Use loop plot to plot total effect of X on Y for low, med, high values of W
! NOTE  values of 1,5 in LOOP() statement need to be replaced by
! logical min and max limits of predictor X used in analysis
PLOT(LOMOD MEDMOD HIMOD);
LOOP(XVAL,1,5,0.1);
LOMOD = (b0 + a0*b1 + cdash2*LOW_W) + TOT_LO*XVAL;
MEDMOD = (b0 + a0*b1 + cdash2*MED_W) + TOT_MED*XVAL;
HIMOD = (b0 + a0*b1 + cdash2*HIGH_W) + TOT_HI*XVAL;
PLOT:
TYPE = plot2;
OUTPUT:
STAND CINT(bcbootstrap);
Return to Model Template index.
To cite this page and/or any code used, please use:
Stride, C.B., Gardner, S., Catley, N. & Thomas, F.(2015) 'Mplus code for the mediation, moderation, and moderated mediation model templates from Andrew Hayes' PROCESS analysis examples', http://www.offbeat.group.shef.ac.uk/FIO/mplusmedmod.htm
