## 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.