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Dear All, I would be grateful if someone could explain me what is the "true" difference between the random effect models and the latent growth curve models. For me they are equivalent ... It seems to me that the difference comes from the fact that these two models are used in separate fields. If the answer to my querry is that the random effect model is a particular case of the latent growth curve model, then I would be grateful to know which practical situations one can use the latent growth curve model but not the random effect model? Thanks a lot, Abderrahim 


The multilevel (HLM) and mixed linear (SAS PROC MIXED) models are identical. The SEM random effects model differs from those models in two ways: the SEM model has time scores as parameters rather than data and timevarying covariates cannot be random in the SEM model. If you want to see a comparison of the formulas for the three models, you can purchase the latest Day 2 short course handout where this is described on slides 2532. Note that in Mplus, time scores can be treated as parameters or data (individuallyvarying times of observation) and timevarying covariates can be random. 


To clarify when I said random timevarying covariates, I meant random slopes for timevarying covariates. 


Dear Linda, Thanks a lot for your thoughts. But I still have a problem in understanding your statement. First, the linear mixed effects model are also considered as conditional models in the sense that covariates are considered as exogeneous and no distributional assumptions are made for them. My question still holds: In which practical situations one can use the latent growth model but not the linear mixed effect model. PS: When I speak about the linear mixed effect model I speak about the modelling point of view and not the software used to estimate it. I would also to thank you for this interesting discussion list. Thanks a lot, Abderrahim 


I am speaking of the models not the software. I refer to the software for those not familiar with the names of the model. As I said earlier, the three models are statistically equivalent with the exception that in the SEM model time scores are treated as parameters and in the SEM model the slopes of timevarying covariates cannot be random. The Mplus model offers users a choice of treating time scores as data or parameters and of having random or fixed slopes for timevarying covariates. 


Does the Mplus fit the latent growth curve for the categorical ordered data in the follwing case: The data is unbalanced in the sense that every subject has its proper number of observations and his proper time points (e.g. age)? can we introduce the direct and indirect (mediated)effect of timevarying and timeinvariant covariates ? Thanks in advance Abderrahim 


Yes and yes. 


I am also wondering if you have some books (that my department can order) containing models used in Mplus with chapters containing theoritical details (specification, identification and estimation)? Thanks Abderrahim 


The Mplus User's Guide has many examples. Some have references. All of these examples and Monte Carlo counterparts of most are available on the website and also come with the Mplus CD. Some theoretical specifications can be found in the Technical Appendices which are also on the website. There are also many references on the website for the various methods. If you are interested in a particular type of modeling, for example, growth modeling, the references on the website should be helpful. 

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