I've noticed this issue coming up a lot in statistical consulting settings and i was keen to get your thoughts.

### Context

I often speak to research students that have conducted a study approximately as follows: 

* Observational study
* Sample size might be 100, 200, 300, etc.
* Multiple psychological scales have been measured (e.g., perhaps anxiety, depression, personality, attitudes, other clinical scales, perhaps intelligence, etc.)

The researchers have read the relevant literature and have some thoughts about possible causal processes.
Often there will be some general conceptualisation of variables into antecedents, process variables, and outcome variables.
They have also often heard that structural equation modelling is more appropriate for testing overall models of the relationships between the set of variables that they are studying.

### Question

* Under what conditions do you think structural equation modelling is an appropriate technique for analysing such studies?
* If you would not recommend structural equation modelling, what alternative techniques would you recommend?
* What advice would you give to researchers considering using structural equation modelling in such cases?