I am asked by colleagues some help in this subject, that I don’t really know. They made hypotheses on the role of some latent variables in one study, and a referee asked them to formalize this in SEM. As what they need doesn’t seem too difficult, I think I’ll give it a shot … for now, I am just looking for a good introduction to the subject!
Google wasn’t really my friend on this.
PS: I read Structural Equation Modeling
With the sem Package in R by John Fox, and this text by the same author. I think this can be sufficient for my purpose, anyway any other references are welcome.
Best Answer
I would go for some papers by Múthen and Múthen, who authored the Mplus software, especially
(Available as PDFs from here: Weighted Least Squares for Categorical Variables.)
There is a lot more to see on Mplus wiki, e.g. WLS vs. WLSMV results with ordinal data; the two authors are very responsive and always provide detailed answers with accompanying references when possible. Some comparisons of robust weighted least squares vs. ML-based methods of analyzing polychoric or polyserial correlation matrices can be found in:
For other mathematical development, you can have a look at:
Sophia Rabe-Hesketh and her colleagues also have good papers on SEM. Some relevant references include:
Other good resources are probably listed on John Uebersax's excellent website, in particular Introduction to the Tetrachoric and Polychoric Correlation Coefficients. Given that you are also interested in applied work, I would suggest taking a look at OpenMx (yet another software package for modeling covariance structure) and lavaan (which aims at delivering output similar to those of EQS or Mplus), both available under R.