I am working on AR(1) DSEM models. Some of my outcome variables at level2 are not normally distributed, but rather zero-inflated. When I run my model, the PSR is absurdly high (728.312). In other models I obtain satisfactory PSR values, but still have problematic estimated parameters according to the autocorrelation plots. My questions are : 1. What can I do to improve model convergence ? 2. Is there a way to treat zero-inflated data properly within the DSEM ? 3. How to deal with unsatisfactory diagnostic plots for autocorrelations (mainly for estimated parameters related to the zero-inflated variables)?
Here is a (truncated) input for a model with very high final PSR :
You most likely don't need RW - if Mplus says you do, then you have most likely not specified the model correctly
Because of your random AR, make sure you have at least 10-20 time points for most subjects and that most subjects change over time
When you say zero-inflated data, perhaps you mean a strong floor effect (and not counts). Such as with negative affect. There are ways to handle this using two-part modeling and we are in the process of writing an applied paper on this.
If this doesn't help, send your output and data to Support along with your license number.
Thank you, it worked ! If I have convergence issues with subsequent models, I will contact the support service.
I do have counts data for one of my outcome variables (from 0 to 6). I'm looking forward to read your paper on the subject. In the meantime, did you already present how to treat these kind of data during one of the webinars or elsewhere ?