As yet I haven’t convinced myself that SEM is a good idea—or at least not in the examples I’ve seen in psychology. Two reasons for starters: (i) fit statistic chaos and (ii) weird analyses driven almost entirely by correlations or at best with vague theorizing based on trivial analyses of the tasks (it has a bit of this and bit of that…).
Anyway, recently Andrew Gelman noted:
“… there’s a research paradigm in which you fit a model—maybe a regression, maybe a structural equations model, maybe a multilevel model, whatever—and then you read off the coefficients, with each coefficient telling you something. You gather these together and those are your conclusions.
“My paradigm is a bit different. I sometimes say that each causal inference requires its own analysis and maybe its own experiment. I find it difficult to causally interpret several different coefficients from the same model.”
He goes on in the discussion to add:
“I have no problem with multiple-equation models, including measurement-error models, multilevel models, and instrumental variables. But I’m skeptical of trying to answer several casual questions by fitting a single model to a dataset.”
Interesting discussion starting over here.