We wish to test a bivariate Cholesky model, but using a CFA of raw items as input (rather than scale scores).
What springs to mind is to build a raw-data RAM-based CFA, and then use the individual-level cells representing latent variable scores as input to matrix-style models in a twin analysis.
The RAM models optimise against the raw data, and the Matrix models would take no data, and optimise against the RAM latent factors.
Does that make sense? Any examples floating around? Or alternative approaches (hierarchical factor model and Cholesky all in one, I guess?