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Mplus Discussion > Growth Modeling of Longitudinal Data >
 Yuchun Peng posted on Wednesday, February 16, 2011 - 7:48 am
Hi Bengt and Linda,
I am running LCGA models with binary variables (call b1-11- 11 time points). As I know, Mplus defaults the thresholds of the binary variable at the 11 time points. My questions is

1) Does it make sense to try time-specfic unequal threshold and cluster-specific unequal threshold? Will it improve the model fit?

e.g. time-specific unequal threshold
[b1$1] (1);


e.g.cluster-specific unequal threshold
[b1$1] ;
[b1$1] ;

2) If it does make sense, is there any test I can do to make sure I need this complex model?

3) The linear quadratic growth factors for Cluster 1 are not significant? I know they are meaningless for binary outcomes, but do I still need to specify them as 0?


 Bengt O. Muthen posted on Wednesday, February 16, 2011 - 10:12 am
Using time-specific thresholds would not give you a growth model because a growth model assumes invariant measurement across time.

Instead of cluster-specific (that is, latent class specific) thresholds you should use cluster-specific growth factor means.
 Yuchun Peng posted on Wednesday, February 16, 2011 - 1:26 pm
Hi Bengt,
Thanks for your answer. But, two things I am not sure are that 1) do I need to specify a zero cluster growth factor if it is not significant?

The second thing I am not sure, how these clusteer-specific growth factor means link to thresholds ? How they derive estimated probability for each category?

Thanks for answering my basic questions,

 Bengt O. Muthen posted on Wednesday, February 16, 2011 - 6:25 pm
Unless you have theory for it, I wouldn't get rid of say a quadratic growth factor if it is insignificant in one latent class. Just report this.

Your last question takes a latent class course to go through well - you should study the literature and our videos. Briefly, take for example the intercept growth factor mean [i]. This influences the probability of say a binary item at the time point where the time score is zero. It does so via the logit as

logit = -threshold + [i].
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