many thanks for your answers.However, I am still a bit confused about 1. the estimated parameters of a latent class predictor and 2. the nature of latent class predictors: 1. In regression we know that although the means, variances, and covariances of predictors are usually not shown in an output they need to be estimated. Why did I have to set these to 0 when I was predicting latent classes? 2. I tried to predict class membership with a latent variable that had 5 indicators. Logics would have it that this factor has a mean and a variance (cf. 1.) and that its indicators have intercept 0 and residual variance. That did not work. Then I tried all combinations of setting the factor mean, the factor variance, the indicators´intercepts, and the indicators´ residual variances to 0 but nothing work. Is there an example of a latent variable predicting class membership (e.g., variable x in example 8.1 being latent)?