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finnigan posted on Monday, May 12, 2008  7:17 am



I am using a multiple indicator growth model. I suspect that there will be some heterogenity within the sample and so I'm adding a latent class(es) to examine this. The model outlined for example 8.1 of The MPLUS users guide(V.5.1) is a good approximation of what I'm trying to do. Are there any issues surrounding the addition of latent classes to a multiple indicator growth models? Am I correct in saying the measurement invariance will be required prior to the addition of the covariates and latent class(es). Thanks 


This type of modeling is possible, but there are important issues of measurement invariance to be considered. For a recent overview, see Muthén, B. (2008). Latent variable hybrids: Overview of old and new models. In Hancock, G. R., & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models, pp. 124. Charlotte, NC: Information Age Publishing, Inc. which is on our web site. Essentially, if there is a need for latent classes, these often create measurement noninvariance such that growth modeling is complicated. Growth modeling needs to consider a dependent variable that is in the same metric and has the same meaning across time. 

finnigan posted on Tuesday, May 13, 2008  1:41 pm



Dear Bengt I read the very informative review. However, I don't understand how the addition of latent classes may create measurement non invariance. I would appreciate any steer you might have on this point. Thanks 


Typically, the measurement intercept (or thresholds) are not invariant across the latent classes. If the latent classes differed only in factor means, but not measurement intercepts, you would have measurement invariance, but this is far less often the case. 

RuoShui posted on Thursday, December 12, 2013  6:24 pm



Dear Bengt, It is quite helpful reading the above posts. I am also trying to find out if there are latent classes after analyzing my data using LGCM with multiple indicators. You said "Typically, the measurement intercept (or thresholds) are not invariant across the latent classes." I am not quite sure if I understand you correctly. Will using the following syntax to constrain everything to be equal across latent classes (except for growth factor means) solve the concern of measurement noninvariance? Thank you very much! USEVARIABLES ARE a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 c1 c2 c3 c4 c5; Classes=c(2); MODEL: %Overall% f1 BY a1 a2 (1) a3 (2) a4 (3) a5 (4); f2 BY b1 b2 (1) b3 (2) b4 (3) b5 (4); f3 BY c1 c2 (1) c3 (2) c4 (3) c5 (4); [a1 b1 ](5); [a2 b2 ](6); [a3 b3 c3](7); [ b4 c4](8); [ b5 c5](9); i s  f1@0 f2@1 f3@2; Analysis: Type=Mixture; Algorithm=Integration; 


You are holding the measurements invariant across classes, including some measurement intercepts. This makes it possible to identify class differences in the growth factor means. But it doesn't solve the concern  this model may fit considerably less well than a model with measurement intercepts varying across classes. 

RuoShui posted on Saturday, December 14, 2013  6:12 pm



Dear Bengt, Thank you so much for the explanation. Considering such a limitation of either measurement noninvariance across classes or worsen model fit by holding measurement invariant across classes, is it practically possible to run GMM with multiple indicators? Or is it recommended to always try to run GMM with observed outcomes? Thank you so much for your time. 


You can certainly run GMM with multiple indicators, it is just that you have many more model variations to explore  more that you can learn about your data. 

RuoShui posted on Tuesday, December 17, 2013  3:45 am



Thank you very much Bengt. I really appreciate your time and the discussion board! 

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