Mplus code for the mediation, moderation, and moderated mediation model templates from Andrew Hayes'
PROCESS analysis examples
Model 1b: 1 moderator [BASIC MODERATION], dichotomous moderator
Example Variables: 1 predictor X, 1 moderator W, 1 outcome Y
Preliminary notes:
The code below assumes that
 The primary IV (variable X) is continuous or dichotomous
 The moderator (variable W) is dichotomous. 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 + b1X + b2W + b3XW
Algebra to calculate indirect and/or conditional effects by writing model as Y = a + bX:
Y = b0 + b1X + b2W + b3XW
Hence... grouping terms into form Y = a + bX
Y = (b0 + b2W) + (b1 + b3W)X
Hence...
One direct effect of X on Y, conditional on W:
b1 + b3W
so inserting the values of 0 and 1 for moderator W gives....
when W = 0, Y = b0 + b1X; when W = 1, Y = (b0 + b2) + (b1 + b3)X
Mplus code for the model:
! Predictor variable  X
! Mediator variable(s) – (not applicable)
! Moderator variable(s)  W, dichotomous, coded 0/1
! Outcome variable  Y
USEVARIABLES = X 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 X (b1);
Y ON W (b2);
Y ON XW (b3);
! Use model constraint subcommand to test simple slopes
! You need to insert your two moderator values, 0 and 1
MODEL CONSTRAINT:
NEW(LOW_W HIGH_W SIMP_LO SIMP_HI);
LOW_W = 0;
HIGH_W = 1;
! Now calc simple slopes for each value of W
SIMP_LO = b1 + b3*LOW_W;
SIMP_HI = b1 + b3*HIGH_W;
! Use loop plot to plot model 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 HIMOD);
LOOP(XVAL,1,5,0.1);
LOMOD = (b0 + b2*LOW_W) + (b1 + b3*LOW_W)*XVAL;
HIMOD = (b0 + b2*HIGH_W) + (b1 + b3*HIGH_W)*XVAL;
PLOT:
TYPE = plot2;
OUTPUT:
STAND CINT(bcbootstrap);
If you are feeling confident you could simplify the MODEL CONSTRAINT code to:
MODEL CONSTRAINT:
NEW(SIM_MOD0 SIM_MOD1);
SIM_MOD0 = b1;
SIM_MOD1 = b1 + b3;
! Use loop plot to plot model for values of W = 0, W = 1
! 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(MOD0 MOD1);
LOOP(XVAL,1,5,0.1);
MOD0 = b0 + b1*XVAL;
MOD1 = (b0 + b2) + (b1 + b3)*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
