# Problems (potentially) caused by multicollinearity in SEM?

I am currently running a SEM in Mplus 7.0. In this, I have four independent latent constructs, measured by 4, 4, 4 and 10 variables. The latent constructs correlate .67-.80. These constructs should indeed be highly related; one is an older construct used in a lot of other research, the other 3 are theoretically distinct subdimensions of a closely related yet different construct. CFA supports this. Raykov composite reliability for all measures is high, .75-93.

I simultaneously linearly regress a number of other latent variables on these independent constructs, while allowing covariance between the predictor variables. Estimation uses MLR since I have non-normally distributed variables.

Running such a model has reasonably low explained variances (R^2 between .25 and .50, with an extreme case of .06), and in some cases some quite unexpected results (e.g. negative sign where a positive would probably be expected). While I have heard that sign reversal can be symptomatic of multicollinearity in non-SEM regression, I do not know if this is also a concern for SEM. I have also failed to find much literature on the topic, but what I have found tends to suggest only that standard errors would inflate, thereby leading to type 2 error. The sole simulation study I found suggested that under high composite reliability and sample size, the probability of this is strongly reduced (Grewal, Cote and Baumgartner, 2004).

Should I nonetheless be concerned that these results are indicative of too strongly multicollinear variables?

First, I wouldn't call an $$R^2$$ of between 0.25 and 0.50 low.