What does it mean to correlate residuals in SEM? I have been reading a paper by Cole, Ciesla, and Steiger, which argues in many cases allowing residuals to correlate is justified. However, I am not entirely sure what it means for residuals to correlate. A good definition and example would be much appreciated. 
Cole, D. A., Ciesla, J. A., & Steiger, J. H. (2007). The insidious effects of failing to include design-driven correlated residuals in latent-variable covariance structure analysis. Psychological Methods, 12, 381–398. 
 A: It means that the unexplained variance  from two variables are correlated. One way of thinking of this is as a partial correlation.
Say we have two regression equations:
\begin{equation}
Y_1i=\beta1_1 \ X_i+\epsilon1_i
\end{equation}
\begin{equation}
Y_2i=\beta2_1 \ X_i+\epsilon2_i
\end{equation}
Both equations have an $\epsilon$ term. If you model that as two equations, that's fine. But what if you model it as one equation - do you want to assume that the $\epsilon$ terms are uncorrelated? If you do, then don't correlate them - as in, don't put estimate a correlation in the residual. Usually you don't, so you'd correlate the residuals.
An example: Say you want to look at the effect of age (in adults) on: speed at running 100m, speed at running 5 miles.  I'd expect a negative relationship for both of these, but if you modeled them in one equation, you'd expect unexplained variance in 100m running speed to be correlated with 5 mile running speed, controlling for age - so the residuals are correlated.
You can also think of this in terms of latent variables - there are common causes of the residual for both 100m and 5 mile speeds, and hence you can hypothesize the existence of a latent (unmeasured) variable.
