Hi. I have clustered data that I would like to analyze. At the individual level, I have some measured binary covariates and 4 factors (measured by 12 variables), with a binary outcome. At the group level, I have 2 factors (one that is the same as a level-1 factor, one that is made up of individual measures + group-level only measures), and other continuous measured covariates.
My theory concerns this level-1 binary outcome as predicted by both level-1 and level-2 variables, and then hoping to see if cross-level mediated relationships (e.g., 2-1-1, 2-2-1-1) exist. My model also involves multiple endogenous variables (so various 2-2-2 or 1-1-1 relationships as well).
Does this kind of problem sound like something Mplus can handle? One of the biggest mysteries to me in SEM is how the intercepts fit in the regression analyses. I am more familiar with HLM in which I would predict the level-1 intercept using level-2 predictors -- does Mplus accommodate intercept-as-outcome modeling, or is this framework not one in which I should be conceptualizing when doing the HLVSM?
Thank you, but how exactly do I specify that in my syntax? Do I just take a factor that I specify in the WITHIN part of my model and use it as a dependent variable in my BETWEEN equation for it to be modeled as the intercept-as-outcome equation?
A factor does not have a random intercept on between. Observed variables on within have a random intercept on between. You would need to declare a factor on between. See the examples in Chapter 9 of the user's guide.