﻿figure it out - a statistical consultancy from the Institute of Work Psychology, University of Sheffield

## Mplus code for mediation, moderation, and moderated mediation models

Model 15 (latent variable version): 1 or more mediators, in parallel if multiple (example uses 1), 1 moderator of both Mediator-DV and direct IV-DV path

Example Variables: 1 latent predictor X measured by 4 observed variables X1-X4, 1 latent mediator M measured by 4 observed variables M1-M4, 1 latent moderator V measured by 4 observed variables V1-V4, 1 latent outcome Y measured by 4 observed variables Y1-Y4

Preliminary notes:

The code below assumes that

• The latent IV (factor X) is measured by continuous observed variables X1-X4.
• Any latent moderator(s) (factors W, V, Q, Z) are measured by continuous observed variables W1-W4, Z1-Z4, V1-V4, Q1-Q4 respectively.
• Any latent mediator(s) (factor M, or factors M1, M2, etc.) are measured by continuous observed variables M1-M4 or M1_1-M1-4, M2_1-M2_4 respectively.
• The latent outcome Y is measured by continuous observed variables Y1-Y4.

Model Diagram (factor indicator variables omitted for space/clarity reasons):

Statistical Diagram (factor indicator variables omitted for space/clarity reasons):

Model Equation(s):

Y = b0 + b1M + b2MV + c1'X + c2'V + c3'XV
M = a0 + a1X

Algebra to calculate indirect and/or conditional effects by writing model as Y = a + bX:

Y = b0 + b1M + b2MV + c1'X + c2'V + c3'XV
M = a0 + a1X

Hence... substituting in equation for M

Y = b0 + b1(a0 + a1X) + b2(a0 + a1X)V + c1'X + c2'V + c3'XV

Hence... multiplying out brackets

Y = b0 + a0b1 + a1b1X + a0b2V + a1b2XV + c1'X + c2'V + c3'XV

Hence... grouping terms into form Y = a + bX

Y = (b0 + a0b1 + c2'V + a0b2V) + (a1b1 + a1b2V + c1' + c3'V)X

Hence...

One indirect effect(s) of X on Y, conditional on V:

a1b1 + a1b2V = a1(b1 + b2V)

One direct effect of X on Y, conditional on V:

c1' + c3'V

Mplus code for the model:

! Latent predictor variable X measured by X1-X4
! Latent mediator M measured by 4 observed variables M1-M4
! Latent moderator V measured by 4 observed variables V1-V4
! Latent outcome variable Y measured by Y1-Y4

USEVARIABLES = X1 X2 X3 X4 M1 M2 M3 M4
V1 V2 V3 V4
Y1 Y2 Y3 Y4;

ANALYSIS:
TYPE = GENERAL RANDOM;
ESTIMATOR = ML;
ALGORITHM = INTEGRATION;

! In model statement first state measurement model
! Then create any latent interactions required
! Then state structural model naming each path and intercept using parentheses

MODEL:

! Measurement model
! This makes these factors standardised
X BY X1 X2 X3 X4;
M BY M1 M2 M3 M4;
V BY V1* V2 V3 V4;
Y BY Y1 Y2 Y3 Y4;

V@1;

! Create latent interactions
MV | M XWITH V;
XV | X XWITH V;

! Fit structural model and name parameters
! Note that intercepts of M, Y are fixed = 0 since they are latent vars
! so no code to state and name them as parameters
Y ON M (b1);
Y ON MV (b2);

Y ON X (cdash1);
Y ON V (cdash2);
Y ON XV (cdash3);

M ON X (a1);

! Use model constraint subcommand to test conditional indirect effects
! You need to pick low, medium and high moderator values for V
! for example, of 1 SD below mean, mean, 1 SD above mean

! 1 moderator, 3 values for it
! arbitrary naming convention for conditional indirect and total effects used below:
! MED_Q = medium value of Q, etc.

MODEL CONSTRAINT:
NEW(LOW_V MED_V HIGH_V
IND_LOWV IND_MEDV IND_HIV
IMM
DIR_LOWV DIR_MEDV DIR_HIV
TOT_LOWV TOT_MEDV TOT_HIV);

LOW_V = -1;   ! -1 SD below mean value of V
MED_V = 0;   ! mean value of V
HIGH_V = 1;   ! +1 SD above mean value of V

! Calc conditional indirect effects for each combination of moderator values
! and index of moderated mediation

IND_LOWV = a1*b1 + a1*b2*LOW_V;
IND_MEDV = a1*b1 + a1*b2*MED_V;
IND_HIV = a1*b1 + a1*b2*HIGH_V;

IMM = a1*b3;

! Calc conditional direct effects for each combination of moderator values

DIR_LOWV = cdash1 + cdash3*LOW_V;
DIR_MEDV = cdash1 + cdash3*MED_V;
DIR_HIV = cdash1 + cdash3*HIGH_V;

! Calc conditional total effects for each combination of moderator values

TOT_LOWV = IND_LOWV + DIR_LOWV;
TOT_MEDV = IND_MEDV + DIR_MEDV;
TOT_HIV = IND_HIV + DIR_HIV;

! Use loop plot to plot conditional indirect effect of X on Y for each combination of low, med, high moderator values
! Could be edited to show conditional direct or conditional total effects instead
! NOTE - values from -3 to 3 in LOOP() statement since
! X is factor with mean set at default of 0

PLOT(LOMOD MEDMOD HIMOD);

LOOP(XVAL,-3,3,0.1);

LOMOD = IND_LOWV*XVAL;
MEDMOD = IND_MEDV*XVAL;
HIMOD = IND_HIV*XVAL;

PLOT:
TYPE = plot2;

OUTPUT:
CINT;