Structural equation modeling vs. mixed effects model I have a question about the difference between structural equation modeling and mixed effects models.
What I'm trying to do is to use anatomical data (e.g. volume of a certain brain region) to explain performance in a cognitive test while controlling for age, sex, education level etc. I want to know how strongly the brain region volume influences performance and ideally to make an inference about causality by controlling for other factors. SEM and mixed effect models seemed like appropriate methods to me but I don't see how they would give me different information.
I'd appreciate if someone could explain this, or recommend any reading about the comparison of these methods, or maybe you want to suggest a different method altogether (e.g. to find out how much of the variance of cognitive performance is explained by that brain region volume). Thanks!
EDIT: To be more precise in case it matters: there are 5 different cognitive tests and 5 different anatomical measures, both sets have been measured twice (different years). I don't know if there is a good way to incorporate everything into one analysis or if I need to do 5x5 analyses.

Structural equation models and mixed effects models can be equivalent. My favorite reference on this is: Have Multilevel Models Been
Structural Equation Models All Along? by Curran.
What analysis is best depends on what hypothesis you want to test. It's not possible to say without more details.
