Mediated moderation
Message/Author
 Alcohol Study posted on Wednesday, April 13, 2011 - 11:47 am
Hello Drs. Muthen,

I am running a path analysis, specifically a mediated moderation using the following model:

MODEL:
VAS3_anx ON VAS1_anx cfne_m ccond fnexcond;
IAT_TR on VAS3_anx;

where VAS3_anx is mood state at time 2, VAS1_anx is mood state at time 1,
cfne_m is a measure of social anxiety,
ccond is condition,
fnexcond is the mood condition and social anxiety interaction term and
IAT_TR is tension reduction cognitions.

I am consistently getting CFI values at 1 and TLIs above one and am pretty sure my model is not saturated.

How do I interpret these CFI values, especially given the fact the VAS3_anx to IAT_TR relation is non-significant?

Thank you
 Linda K. Muthen posted on Wednesday, April 13, 2011 - 12:22 pm
What is your chi-square value and p-value and how many degrees of freedom does the model have?
 Alcohol Study posted on Wednesday, April 13, 2011 - 2:36 pm
I have 4 dfs, and the pvalue is 0.0000, with a CFI of 1.000 and a TLI of 1.054.
 Alcohol Study posted on Wednesday, April 13, 2011 - 2:40 pm
Oops, sorry, my Chi square is 1.139, p=0.888.

for the baseline model it is
127.366, df=9, p=0.0000

the P value for IAT_TR on VAS3_anx is .590.
 Linda K. Muthen posted on Wednesday, April 13, 2011 - 2:57 pm
So your model fits very well given the chi-square p-value and CFI and TLI. This can also happen if you have very low sample correlations and a very small sample size with low power to reject the model.
 Guanyi Lu posted on Wednesday, May 30, 2012 - 8:02 am
Hi Dr. Muthen,

I am testing a moderated mediation model.

I use "xwith" to create the interaction term (one continuous latent and one observed continuous). "type=random" was used.

bootstrapping + confidence interval were used to test mediation effects.

Mplus 6.21 gave me an error message stating that "model indirect" cannot be used with "type=random". However, "xwith" can only be used with "type=random".

I wonder is there a way to test mediation effects while keeping the interaction term (xwith command) in my model? in other words, can I test both mediation and moderation in ONE model using Mplus?

Best,

Komen
 Linda K. Muthen posted on Wednesday, May 30, 2012 - 10:26 am
You can use MODEL CONSTRAINT to specify the indirect effects. See the user's guide for further information.
 Guanyi Lu posted on Wednesday, May 30, 2012 - 12:11 pm
Thanks Linda.

Using "bootstrapping and bias corrected CI" (the method proposed by Hayes and Preacher) to test mediation is recommended by the reviewers. I want to stick to this method.

If I use "model constraint" and "bootstrapping + bias corrected CI", I guess I will get CIs for the indirect effects specified.

Are those CIs equivalent to CIs which are produced by Mplus when the interaction term is not introduced?
 Linda K. Muthen posted on Thursday, May 31, 2012 - 12:10 pm
The confidence intervals with and without the interaction will not be the same. The model has changed.
 Kathrin Albrecht posted on Monday, August 08, 2016 - 10:39 am
Hello Dr. Muthen,

I also have question regarding mediated moderation with MPLUS. My data comes from an experimental design (IV and Moderator are manipulated variables, each has two conditions) and I want to calculate a model with the following variables:
1 IV (observable, categorial, two groups), 1 Moderator (observable, categorial, two groups), 1 Mediator (latent, continuous), 3 DVs (each continuous and latent). The moderator influences the path from the IV to the Mediator.

I found an instruction that equals to Model 7 in PROCESS Macro: http://offbeat.group.shef.ac.uk/FIO/model7.htm

However, as I am not very familiar with MPLUS yet, I am not sure, if this is the right way. Another problem is that I can only include 1 DV in the model. But I am interested in simultaneously estimating the model with all 3 DVs (if this is possible at all).

Best
Kathrin
 Bengt O. Muthen posted on Monday, August 08, 2016 - 1:17 pm
To handle a binary moderator you can either create a product of variables in Define or use multiple-group analysis.

These types of models are discussed in our new book:

http://www.statmodel.com/Mplus_Book.shtml

http://www.statmodel.com/Mediation.shtml
 Michael Daniels posted on Thursday, September 06, 2018 - 5:11 am
Hi Dr. Muthen,

I would like to test an indirect effect whereby the IV is an interaction between a manifest variable (a dichotomous experimental manipulation) and a continuous latent variable (to test for mediated moderation). The mediator and DV are both continuous latent variables. Is this possible? It seems that the MODEL INDIRECT command does not work when TYPE=RANDOM. Is this model possible?

Thanks for any help.
 Linda K. Muthen posted on Thursday, September 06, 2018 - 1:53 pm
You need to use MODEL CONSTRAINT to specify the indirect effect in this case.
 Tatiana Iolanda Pires Marques posted on Monday, February 17, 2020 - 9:58 am
Hello Drs. Muthen,

I am running a SEM to test a model with 3 mediated moderation effects. Specifically:
INTJ is my IV
Age is the moderator
Trust is the DV
PR_O is the mediator of the effect of age on the relationship between INTJ and Trust

INTJ is my IV
Age is the moderator
SCOM is the DV
REL_SAL is the mediator of the effect of age on the relationship between INTJ and SCOM

INTJ is my IV
Age is the moderator
LOY is the DV
REL_SAL is the mediator of the effect of age on the relationship between INTJ and LOY

All variables are continuous. Age is observed, all others are latent.

The model converge but the fit indexes do not show. Is this normal?

Also, the relationship between moderator and mediator(s) is significant (Age --> PR-O and Age --> REL_SAL) but the Rsquare of PR-O and REL_SAL is not. Is this also normal?

I only get the unstandardized coefficients for the indirect effects. Can I also ask for the standardized?

I will post the code after this.
 Tatiana Iolanda Pires Marques posted on Monday, February 17, 2020 - 10:01 am
Here is the code for my model:

ANALYSIS:
TYPE = RANDOM;
ALGORITHM = INTEGRATION;

MODEL:
PR_O BY PR_O1 PR_O2 PR_O3 PR_O4;
REL_SAL BY REL_SAL1 REL_SAL2 REL_SAL3 REL_SAL4;
INTJ BY INTJ1 INTJ2 INTJ3 INTJ4;
TRUST BY TRUST1R TRUST2 TRUST3R TRUST4;
SCOM BY SCOM1 SCOM2R SCOM3 SCOM4R SCOM5;
LOY BY LOY_D1 LOY_D2 LOY_D3 LOY_D4 LOY_V1 LOY_V2 LOY_V3;

Trust ON Gender Educ Income Workexpy Org_ten Age;
Trust ON INTJ;
Trust ON PR_O;
INTJPRO | INTJ XWITH PR_O;
Trust ON INTJPRO (a);
PR_O ON age (b);

SCOM ON Gender Educ Income Workexpy Org_ten Age;
SCOM ON INTJ;
SCOM ON REL_SAL;
INTJREL | INTJ XWITH REL_SAL;
SCOM ON INTJREL (c);
REL_SAL ON age (d);

LOY ON Gender Educ Income Workexpy Org_ten Age;
LOY ON INTJ;
LOY ON REL_SAL;
LOY ON INTJREL (e);

MODEL CONSTRAINT:
NEW (indirect1);
Indirect1 = a*b;
NEW (indirect2);
Indirect2 = c*d;
NEW (indirect3);
Indirect3 = e*d;

OUTPUT:
sampstat standardized cinterval;

TM
 Bengt O. Muthen posted on Tuesday, February 18, 2020 - 5:05 pm