I am currently trying to create a latent interaction regression model in OpenMx where I have four latent variables (C, H, T, S) that predicts a fifth latent variable (A). In addition, interaction effects between the main latent predictor C, and H, T, and S should be included.
I am trying to figure out how to include interaction effects in SEM. Can I simply multiply the latent indicator variables? Marsh et al. (2012) [7] decribes this as a problematic, and rather recommends a standardized solution first described by Wen, Marsh and Hau (2010). I am unsure how to implement such a model in OpenMx, however.
I'd be grateful for any input on what the best solution for including latent interactions would be, and how to implement them in an OpenMx model.