Whether to use correlation matrix between latent variables or raw data as input to SEM? I have a fairly large model - a total of 8 latent variables and each one has 10 items (observed variables). Drawing this model is a mess (there is a mediation effect as well).
Is it better to just compute correlations among my latent variables and import the correlation table instead of using all the raw data?
 A: For many of the most common applications in SEM, analyzing the raw data is equivalent to analyzing a covariance / correlation matrix.  In these applications, one of the first things your computer program will do is calculate a covariance / correlation matrix.
I should note, though, that analyzing a covariance matrix is not equivalent to analyzing a correlation matrix, and in general analyzing the same data via a covariance matrix and a correlation matrix will yield different standard errors, different confidence intervals around your path coefficients, and different fit statistics.  Which analysis you choose (covariances vs correlations) mostly depends on whether you're interested in the units of your measures.
Patrick
A: This doesn't directly answer your question, since you are using AMOS, but I wanted to point out that there are two approaches to SEM. One uses an approach based on covariances and maximum likelihood, the other uses an approach based on the original data and partial least squares (PLS).
I believe AMOS (LISREL, Mplus, etc) use the first approach, which is generally called SEM. The other approach, often called PLS Path Modeling (or sometimes PLS SEM) is also available in various software, including several R packages. Different horses for different courses, but as I'm learning about the PLS approach, it seems like it has advantages in many applications.
