Hello Krytellan, and thanks for answering so many questions. I am not really expert with SEM but I thought someone should answer since you have made such an effort on other posts.

SEM is essentially a regression with a non identity covariance matrix. The parameterization of the covariance matrix is decided by the causal model or path structure posited by the analyst.

This is almost identical to regression with, say, a mixed model, which is just regression with non identity covariance matrix.

The essential difference (in my narrow view) is that the person using SEM believes that the covariances between observations are caused by something, an observed coavariate or otherwise, while mixed model does not make such inference.

Hope this helps.