a) a vector of 3 observed varaibles measured at 5 time points b) a vector of time invariant covariates influenceing the intercept and slope of the vector in a) c) a scalar time varying covariate influencing the slope and intercept of of a) d) a continuous distal outcome measured at time 6 e) a, b and c varying across classes with mixture indicator d)
my questions are
a) i saw an example of the moel with a distal outcome and no time varying covariate in Hix-Small et al 2004. Is it possible to build the model i am suggesting b) what would be a good reference to a growth mixture model of this nature with a time varying covariate? c) does the distal continuous outcome pose a problem? thanks! hari
Jungeun Lee posted on Wednesday, April 16, 2008 - 4:07 pm
I am hoping to run a growth mixture model which is very similar to the example 8.6 in the Mplus manual. In my model, I'd like to add another distal outcome so that a latent variable 'c' predicts my first distal outcome and then my first distal outcome predicts my final outcome. All observed variables in my model are continuous. Is it possible to run such model in Mplus? If yes, could you point me to examples that I can refer to for a Mplus syntax?
Just remove the CATEGORICAL option from Example 8.6, add the y variable to the USEVARIABLES list, and add y ON u to the MODEL command.
Anne Chan posted on Friday, January 22, 2010 - 6:10 am
Hello, I am planning to run a analysis which is exactly like example 8.6 in the Mplus guide, only the distal outcome in my analysis is a continous variable, but not a binary one. May I ask how to do it?
The setup would be identical to Example 8.6 without the CATEGORICAL option.
Anne Chan posted on Sunday, January 24, 2010 - 5:46 pm
Thanks. I followed the instruction and got the means for each class. I would like to check if there are any significant differences of the distal outcomes between each pair of classes (not comparing all the classes altogether). May I ask is there a way to do so?
Hello, I also want to use the 8.6 example. First question, as my variables are censored, and i don't put Algorithm=integration it's more a LCGA than a GMM that is estimated? In my model, x has a different status. My aim is to estimate the prediction of U (my distal categorical outcome) by C but controlling for the effect of x to estimate if the prediction of U by C still remains significant after controlling for x. I think i must introduce a regression path: u on x; and a covariance between C and x but I'm not sure how to do that. Would that be a proper specification for the model if there is three latent classes ? MODEL: %OVERALL% i s | y1@0y2@1y3@2y4@3; u ON x; c#1 with x; c#2 with x;
I also want to estimate the same model but with the distal outcome being a count variable. Is it ok to specify: COUNT is U; instead of categorical, will the model run properly. Many Thanks! JB
IYH Boon posted on Tuesday, September 09, 2014 - 1:29 pm
I am trying to fit an lcga model that's very similar to the one that JBP describes, above. The idea is to regress a continuous distal outcome, y, onto an indicator of respondents' latent trajectory group, while controlling for a categorical mediating variable, x. I've tried the following Model statement:
Model: %OVERALL% i s q | b1 @ 0 b2 @ .1 b3 @ .2 b4 @ .3 ; y ON x ;
When I run the model, I get the following error: One or more MODEL statements were ignored. These statements may be incorrect or are only supported by ALGORITHM-INTEGRATION.
However, "to regress a continuous distal outcome, y, onto an indicator of respondents' latent trajectory group" would seem to call for e.g.
y on b4;
Also having a categorical mediator - I assume between b4 and y - complicates matter. This is because mixtures use ML and ML does not have access to an underlying continuous mediator as is required - see
Muthén, B. & Asparouhov T. (2014). Causal effects in mediation modeling: An introduction with applications to latent variables. Forthcoming in Structural Equation Modeling.
You can try Bayes estimation which does offer that approach - but that is more advanced.
IYH Boon posted on Wednesday, September 10, 2014 - 11:07 am
Thanks for the quick response; I greatly appreciate it. One follow-up question:
What if my goal was to determine the relationship between latent trajectories (c) and a distal outcome (y) AFTER controlling for an additional set of variables (x). Would the model statement that I used above be correct?