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| Yuchun Peng posted on Wednesday, February 16, 2011 - 7:48 am
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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
%C#1%
[b1$1] (1);
%C#2%
[b1$1](1);
e.g.cluster-specific 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 |
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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. |
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| Yuchun Peng posted on Wednesday, February 16, 2011 - 1:26 pm
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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,
Vicky |
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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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