Hello sci.stat* readers,
I have been working with SEMs (actually, the models I've been
working with are just Mixed models) and am curious why SEM
practitioners seem to assess model fit by only comparing the
observed and predicted covariance matrices. As opposed to (also)
using statistics based on observed vs. predicted outcomes.
One can have an SEM with a covariance matrix that is fit very
well (or perfectly). Yet, if you compared the predicted and observed
outcomes, it's terrible! So, two things:
1. What is the rationale for ignoring this type of poorly fitting model
(seems, to me, to be a fundamental flaw - even if you are just using
if for "exploration")?
2. Is there software that gives you both kinds of fit statistics?
LISREL 8.3 and PROC CALIS seems to only give those based on
predicted and observed covariance matrix. And PROC MIXED only
allows certain paramaterizations.
Thanks!
(I am aware of SEMNET, but thought I would ask statisticians first.)
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