Hi there. I am trying some mediation path modelling. I have a series of predictors on my DV, some of which are mediated by others. However, two predictors have a bidirectional relationship. In other words
SelfForg on guilt apology recvforg morforg; apology on guilt; recvforg on apology; morforg on apology; guilt on apology;
There is more, but that's the important part. Once I introduce the non-recursive element to the model (guilt on apology;), I can no longer get the strength of each possible mediating path.
model indirect: SelfForg ind apology;
USED to generate a whole bunch of different mediating paths. Now it just tells me the sum total indirect effects and direct effects of apology on self-forgiveness (SelfForg). Does that make sense? What I want is to know how much of the indirect effect goes through each possible pathway (E.g., apology --> recvforg --> selfforg). Is there any way to do that? Or does adding the loop somehow derail our ability to know the strength of the individual mediating pathways (because you could go around in circles forever)?
1. I am not sure this model is identified, but perhaps it is just another way of representing their 3 covariances. You may want to ask on SEMNET. You should check the condition number Mplus prints - if the model is identified, it should be higher than at least 1 to the power of -10. You would also see very large SEs if the model is not identified.
2. When you say MLR I hope you don't mean not declaring the variables on the Categorical list, that is, ignoring their ordinality. WLSMV is probably not great here. Perhaps Bayes - which would be another check on identifiability - see the trace.
Note that with ML/R the predictor in each equation is treated as a continuously scored variable despite it being treated as an ordinal DV.
WLSMV doesn't handle missing data as well and you have to assume normality for the underlying latent response variables. WLSMV does not, however, have the problem of ML/R above. Bayes would be better in this regard.