Mplus code for the mediation, moderation, and moderated mediation model templates from Andrew Hayes'
PROCESS analysis examples
Model 4b: 2 mediators in parallel [BASIC MEDIATION]
Example Variables: 1 predictor X, 2 mediators M1 and M2, 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 + b1M1 + b2M2 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
Algebra to calculate total, indirect and/or conditional effects by writing model as Y = a + bX:
Y = b0 + b1M1 + b2M2 + c'X
M1 = a01 + a1X
M2 = a02 + a2X
Hence... substituting in equations for M1 and M2
Y = b0 + b1(a01 + a1X) + b2(a02 + a2X) + c'X
Hence... multiplying out brackets
Y = b0 + a01b1 + a1b1X + a02b2 + a2b2X + c'X
Hence... grouping terms into form Y = a + bX
Y = (b0 + a01b1 + a02b2) + (a1b1 + a2b2 + c')X
Hence...
Two indirect effects of X on Y:
a1b1, a2b2
One direct effect of X on Y:
c'
Mplus code for the model:
! Predictor variable  X
! Mediator variable(s) – M1, M2
! Moderator variable(s)  none
! Outcome variable  Y
USEVARIABLES = X M1 M2 Y;
ANALYSIS:
TYPE = GENERAL;
ESTIMATOR = ML;
BOOTSTRAP = 10000;
! In model statement name each path using parentheses
MODEL:
Y ON M1 (b1);
Y ON M2 (b2);
Y ON X (cdash); ! direct effect of X on Y
M1 ON X (a1);
M2 ON X (a2);
! Use model constraint to calculate specific indirect paths and total indirect effect
MODEL CONSTRAINT:
NEW(a1b1 a2b2 TOTALIND TOTAL);
a1b1 = a1*b1; ! Specific indirect effect of X on Y via M1
a2b2 = a2*b2; ! Specific indirect effect of X on Y via M2
TOTALIND = a1*b1 + a2*b2; ! Total indirect effect of X on Y via M1, M2
TOTAL = a1*b1 + a2*b2 + cdash; ! Total effect of X on Y
OUTPUT:
STAND CINT(bcbootstrap);
Editing required for testing indirect effect(s) using alternative MODEL INDIRECT: subcommand
MODEL INDIRECT: offers an alternative to MODEL CONSTRAINT: for models containing indirect effects, where these are
not moderated. To use MODEL INDIRECT: instead, you would edit the code above as follows:
First, you can remove the naming of parameters using parentheses in the MODEL: command, i.e. you just need:
MODEL:
Y ON X M1 M2;
M1 M2 ON X;
Second, replace the MODEL CONSTRAINT: subcommand with the following MODEL INDIRECT: subcommand:
MODEL INDIRECT:
Y IND M1 X;
Y IND M2 X;
or just with
MODEL INDIRECT:
Y IND X;
Leave the OUTPUT: command unchanged.
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
