 ## Mplus code for the mediation, moderation, and moderated mediation model templates from Andrew Hayes' PROCESS analysis examples

Model 1c: 1 moderator [BASIC MODERATION], dichotomous moderator (using multigroup method)

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;

! Define groups of moderator W

GROUPING = W (0 = GP0 1 = GP1);

ANALYSIS:
TYPE = GENERAL;
ESTIMATOR = ML;
BOOTSTRAP = 10000;

! In model statement first state basic regression that is being moderated

MODEL:
Y ON X;

! Then restate for each group, naming each group's intercept and slope coefficient
! and fixing residual variances equal

MODEL GP0:
[Y](b0g0);
Y ON X (b1g0);
Y (vary);

MODEL GP1:
[Y](b0g1);
Y ON X (b1g1);
Y (vary);

! Use model constraint subcommand to create and test difference in slopes
! Note that slopes for each group provide simple slopes tests already

MODEL CONSTRAINT:
NEW(b3);

b3 = b1g1 - b1g0;

! 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(LINEGP0 LINEGP1);
LOOP(XVAL,1,5,0.1);

LINEGP0 = b0g0 + b1g0*XVAL;
LINEGP1 = b0g1 + b1g1*XVAL;

PLOT:
TYPE = plot2;

OUTPUT:
STAND CINT(bcbootstrap);