I am modelling pubertal growth using LTA. Two time points with a 4 category latent variable at each time point. The LTA is really nice. But I want to model the effect of the transition from T1 to T2 on a distal outcome. How might this be accomplished? Do I need a second order latent categorical variable, as illustrated in the mover-stayer model?
I have a model like 8.14 running. But if "c" (the second order latent categorical variable) is categorical with, say, two classes, and the distal outcome is a continuous variable (such as a latent factor), won't there be a problem with the regression estimates? By that I mean "c" is not dummy (0,1) or effects (-1,1) coded, but is coded as 1,2. How might that be handled?
The regression of a continuous distal outcome on the second-order catgorical latent variable is reflected by the change of means of the continuous distal outcome across the classes of the second-order categorical latent variable.
Kaigang Li posted on Thursday, May 29, 2008 - 12:12 am
If I have a THREE category categorical distal outcome, U, and three covariates, ie. x1, x2, and x3. How can I write in syntax for looking at the effect of covariates on the U?
Is the following commands correct?
%OVERALL% U on x1 x2 x3; %c#1% [U$1 U$2]; %c#2% [U1$1 U2$2];
Kaigang Li posted on Thursday, May 29, 2008 - 11:59 pm
I have another question. I am reading the chapter "Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling." but I found the examples penn1-8.inp at http://statmodel.com/examples/penn.shtml are not consistent with the ones used in the book. Could you please instruct me how I can use those examples?
Kaigang Li posted on Friday, May 30, 2008 - 12:22 am
One more question.
In the article "Jung, T. & Wickrama, K.A.S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2, 302-317." posted at http://statmodel.com/papers.shtml, the author Specified a single-class latent growth curve model using the following
If you do not obtain the same results for a single-level growth model and a one-class growth model, you should send your two output files and your license number to email@example.com. The results should be identical.
Kaigang Li posted on Friday, May 30, 2008 - 8:24 am
I will double check the results.
Do you have any comments on the question posted on Thursday, May 29, 2008 - 11:59 pm right above the question you answered? Thanks,
Colleagues and I have run a LTA with gender as a known class. The next step in our analysis is to include distal outcomes. We have looked at K. Nylund’s dissertation as a guide of how to include distal outcomes in a latent transition model. Her example is helpful in regards to regressing the distal outcomes on a second order mover stayer variable and on estimating the means for a given class within a wave (e.g., class 3 wave 3). However, we are interested in estimating differences in distal outcomes for different transition configurations, separately for boys and girls. For example, we would like to know if girls that transition from class 1 to class 3 have different means on a distal outcome than girls that transition from class 2 to class 3 or girls that remain stationary across the two time points. Is there a way to estimate this in MPLUS? One important thing to note is that we have previously tried to run a mover stayer model with gender as a known class and that model did not replicate (with 2000 starts). Additionally, we tried to run a mover stayer model with gender as a covariate and that model did not replicate either. Any suggestions on how to examine distal outcomes for different transition configurations would be appreciated. Thank you.
Related to the above described LTA models -- I have simplified the model and have everything working in terms of assessing distal outcomes by class consistent with K. Nylund's dissertation approach. To compare distal outcomes across classes I am using the model test approach. However, I cannot seem to locate the actual statistical tests in the output. Can you clarify where these are located (happy to send the output, if that would help).
I am using the manual 3-step approach to estimate a multiple-group LTA with a continuous distal outcome (two classes at two time points assuming measurement invariance, so four latent statuses). In the third step, I successfully obtain distal means for each of the four latent statuses separately for each of the two observed groups. Of the eight means estimated, six are as expected, but two are quite different from the others (two of the four within a single group). Is there a way to manually estimate these latent status means from the data file created in step 2? I'm trying to get a handle on why the means for those two statuses are so different from the rest. I appreciate any guidance you can provide.
Appendix I of our 3-step paper shows that the "c2.dat" data set contains n1 and n2, which are the most likely class variables for the two latent class variables. You can get the distal outcome mean for each cross-classification of n1 and n2.
Thanks for your quick reply. The means estimated in step 3 do no match the means I get from averaging the distal outcomes within each status. For example, for group 2, the step 3 output displays means of -8.83, -43.55, 40.82, -3.21 for statuses 1-4, respectively. Manually estimated means (exporting status and summarizing in Stata) are -6.51, -8.52, -6.25, and -5.47. The -43.55 and 40.82 are the "quite different" means in question. Thoughts?
You may want to check your steps in detail against the Nylund et al (2014) 3-step LTA article on our website. Also check that the class formations stay the same. If that doesn't help, please send data and outputs from your steps to Support so we can see what's happening.
To follow up on this thread, the user did not use the proposed steps: To study the influence of a covariate on the latent class variables in an LTA with measurement invariance, one should go by the Appendix K-N setups, which takes the measurement invariance approach. See the Appendices of the paper on our website:
Asparouhov, T. & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21:3, 329-341. The posted version corrects several typos in the published version. An earlier version of this paper was posted as web note 15. Appendices with Mplus scripts are available here.