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Yuchun Peng posted on Wednesday, February 16, 2011  7:48 am



Hi Bengt and Linda, I am running LCGA models with binary variables (call b111 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 timespecfic unequal threshold and clusterspecific unequal threshold? Will it improve the model fit? e.g. timespecific unequal threshold %C#1% [b1$1] (1); %C#2% [b1$1](1); e.g.clusterspecific unequal threshold %C#1% [b1$1] ; %C#2% [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? Thanks Vicky Thanks 


Using timespecific thresholds would not give you a growth model because a growth model assumes invariant measurement across time. Instead of clusterspecific (that is, latent class specific) thresholds you should use clusterspecific 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 clusteerspecific growth factor means link to thresholds ? How they derive estimated probability for each category? Thanks for answering my basic questions, Vicky 


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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