SEM: Combining latent and manifest variables I did a survey, using many intervall-scaled items representing several constructs. I want to use these constructs to predict an outcome (decision: yes/no).
Thus, I got (latent) predictors to explain a manifest, categorial outcome. 
Can you suggest an adequate SEM-framework? I considered using a latent class analysis. Is this the right way to go?
 A: Latent class analysis assumes that the manifest variables are categorical, but the latent is continuous. In your case, it's the predictor that is categorical. So latent class analysis does not apply to the SEM portion of your model. 
You could use a latent class analysis on the measurement piece, by modeling the ordinal manifests as such. People often pretend that the likert type items (or whatever you have) are "normal" and proceed in the usual way. But you could use latent class analysis here, especially if the number of options for each manifest is small (say, 3). 
Latent class analysis requires a larger sample size to work than the usual SEM model, because it has more work to do. Basically, you assume that each ordinal manifest represents a cut-point from an underlying normal distribution, and you need to estimate those cut-points. I sometimes find that more difficulties emerge from estimating the additional parameters of the latent class model, than by applying normal theory assumptions to ordinal data.
But back to your situation. I agree with @Stijn. I would estimate the factor scores from the predictor variables and throw them into a logistic regression model.
