You need TYPE=RANDOM to specify a long format growth model. This is not available with WLSMV. I suggest specifying your growth model using the wide format. See the examples in Chapter 6. This is also a more flexible model as the residual variances are not held equal over time.
Thank you for your reply. After some more reading I noticed that the MLR estimator used by default in my original script actually uses a logistic model while the WLSMV a probit model. I'd actually prefer a logistic solution.
I am mostly interested in assessing if there is an association between satisfaction and topic selected rather than modelling growth. However, is it wrong to use the multilevel modelling above (after specifying TYPE=TWOLEVEL RANDOM)?
The model you specify has no need for TYPE=RANDOM because you have no random effect except for a random intercept which does not require TYPE=RANDOM. See Examples 9.1 and 9.2 in the user's guide. The first is a random intercept model and the second includes a random slope and random intercept.