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