Recently, a book on
*Discovering Structural Equation Modeling Using Stata*
was published by Stata Press
(Alan C. Acock, 2013). It
is well written and introduces main concepts for factor analysis and
structural equation modeling. Datasets and do-files used in the book can be
downloaded from Stata Press.
Some tips about the
Stata 12 SEM package
have been provided elsewhere. And of course, the UCLA server has already
covered some good material about Stata SEM on their
Stata FAQ.

An Overview of Stata’s "sem" for Structural Equation Modeling, by K. Bollen and S. Bauldry, is also a good start. Of course, there are many others presentations available via few Google clicks, e.g. Structural Equation Modeling Using the sem Command and SEM Builder.

Now, Stata has just announced the release of
Stata 13, with extended capabilities for
latent variable modeling and new support for
generalized linear response variables
(`gsem`

command). See, for example, this example with an
ordered logit model.
So I expect more publications related to the use of Stata for psychometric
research in the future, and it would be very helpful to have a report paper
showing the different capabilities of R, Mplus, and Stata on benchmark
datasets. Surely, a direct comparison of gllamm
vs. Stata built-in SEM package would be very insightful as I always found
`gllamm`

to be much to slow to be used in routine tasks (IRT, multi-group
CFA, etc.). Actually, using a combination of `polychoric`

and `factor`

to
carry out a factor analysis on Likert-type items is also very slow.