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?
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?
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.
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).
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?
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?