You would need to start with a parallel process growth model like the one shown in Example 6.13 and add TYPE=MIXTURE; and one latent class variable using the CLASSES option of the VARIABLE command. For that see Example 8.1.
My study follows a cohort-sequential design. I have transformed my data set and it looks like the one shown in page 405 of User's guide. For this data set, I would like to apply FIML for missing data. In order to do this, is it correct to simply write "TYPE=MIXTURE missing;"?
Following your guidance, is it right to include below commands for my study?
I actually tried to run a growth mixture model with above syntax. And I started with a model without covariates.
However, it seems like that I have a problem with convergence.
I keep getting the same warning message as following:
THE ESTIMATED COVARIANCE MATRIX FOR THE Y VARIABLES IN CLASS 1 COULD NOT BE INVERTED. PROBLEM INVOLVING VARIABLE B7. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 2. CHANGE YOUR MODEL AND/OR STARTING VALUES.
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES.
Could you please provide any tips to solve this kind of problem?
I am VERY VERY new to Mplus, and I am having a tough time studying this on my own.
If I want to specify starting values, what kind of procedure is needed?
Daniel Lee posted on Sunday, April 09, 2017 - 6:30 am
I am modeling a parallel growth model...when I run growth models without regressing intercepts and slopes between two growth constructs (e.g., s2 on i1 s1), the growth processes have significant slopes and intercepts. However, when I include "on" statements between intercepts and slopes (e.g., s2 on i1 s1), the slopes are no longer significant. I was wondering if you can help me understand why this might happen? Thank you.
If you don't have ON statements, you are estimating means and variances for the growth factors. With ON statements, you estimate intercepts and residual variances. Check the output. I think this is what you will see.