Building model for SEM: the problematic of highly correlated variables (SEM + Covariance) I have recently come across articles suggesting we should avoid conducting SEM when variables are highly correlated.
I have a model in which X1 and X2 predicts Y through M1 and M2. Which gives me the following indirect path: X1 -> M1 -> Y ; X1 -> M2 -> Y ; X2 -> M1 -> Y ; X2 -> M2 -> Y.
Can I test this model if:

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*X1 and X2 are highly correlated?

*M1 and M2 are highly correlated?

*X1 and M1 are highly correlated?

*M2 and Y are highly correlated?

*What is considered highly correlated? For E.g. what if it is a .80 covariance? Can we still interpret the results? Especially if the problem occurs between M2 and Y?
Finally, any articles on the subject to recommend?

I acknowledge this is a lot of questions. It is hard to find specific information on this and I am just getting more and more confused over time.

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High correlations might lead to interpretational difficulties (but won't necessarily). Why don't you try to fit your model, see if something strange or unexpected happens, and then try to understand that.
The usual problem is that your variables aren't correlated, even though you (or the people you work for) are absolutely convinced that they should be.
