The correct way to specify the covariance is the one that is consistent with what you are modeling. As such, it might vary from study to study. The key question to ask yourself is about local independence: If you could hold the latent variables constant, would you still expect the observed variables to covary (focusing on the pair in question).
If not, then you just have random measurement error separating the observed variables from the latent variables, and the covariance should be modeled at the latent level because it involves the latent attributes themselves. In your case, it looks like it reflects omitted common causes.
Conversely, if so, then that means that the covariance is not related to the attributes but just an artifact of the two observed variables (e.g., common wording in self-report items). In that case, the covaraince should be modeled at the observed level.
Of course, it is possible for both phenomena to be present in a given set of data. In that case, you would need a research design sufficient to allow you to disentangle the covariances at the two levels.
Keith A. Markus
John Jay College of Criminal Justice, CUNYhttp://jjcweb.jjay.cuny.edu/kmarkus
Frontiers of Test Validity Theory: Measurement, Causation and Meaning.http://www.routledge.com/books/details/9781841692203/