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).