A typical SEM could be seen as a combination of a measurement model and a structural model, and all parameters from both models could be estimated simultaneously. An alternative is a two-step way, i.e. first run the measurement model, extract the factor scores (using some regression method, or Bayesian way), then use these factor scores as dependent or independent variables, together with other observed variable, to run a regression model.
I found in some literature that this two-step way could avoid the "interpretational confounding" problem. e.g. the factor loadings in the measurement model at the first step won’t be affected by the structural model at the second step, which is not the case in the simultaneous SEM.
So my question is: how popular is the two-stage way and what is the advantage/disadvantage of it, and when to choose this instead of the simultaneous estimate of SEM? I've checked some papers and books, but they are not very clear to me. Any comments, suggestions, recommended papers/books are appreciated!