

Twin Factor Mixture Modelingfirst step 

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Hello, I have a data set on twins, so that for each twins there are 23 items on a 5point scale (i.e., quasi continuous). I want to perform a FMM, similar to Muthen, Asparouhov, and Rebollo (2006). Before the heritability analysis, I want to decide on the right number of classes and factors. For that I would like to ask a few questions: 1. Is example 7.27 in the version 7 manual is the right example to look at (except that I won't need to specify the items as categorical)? 2. Does this analysis supposed to be done for all twins simultaneously (i.e., should I use also type=complex and organize the data in a 'twin below twin' format)? 3. Is example 7.27 relevant also for the next step, in which I need to compare this model to a model of 2 or 3 factors within each class? Thank you very much in advance 


1. Yes 2. You can do that. You can alternatively do the twins in wide format with equalities of measurement parameters across twins and with freely correlated c and f across twins. 3. Yes. 


Thank you so much for the answers. Your support is really helpful. Regarding (2)from Muthen et al. (2006) I understand that the wide format is preferable and I will follow this recommendation. However, I got complicated syntax wise. Is there any example that could help me in that? If not, I would like to make sure I understand correctly. Lets say I have c1 and f1 for twin1 and c2 and f2 for twin2. A) to ensure equalities of measurement across twins, should I assign the same number in brackets to equivalent observed items on the latent classes? for example: %c1#1% [u11] (1) . . . %c2#1% [u21] (1) . . . B) to let c and f correlate freely across twins, should I just write "c1 with c2" and "f1 with f2" below MODEL:%OVERALL% Thank you again 


A) Yes, but use Model c1: and Model c2: as in UG ex 7.18 B) Yes. 


Hello again, another preliminary step I am supposed to do before turning to the Factor Mixture Analysis, is to examine the number of factors in a CFA. The problem is that CFA is theory driven, and while I have a theory regarding 3 factors and 4 factors, I don't have any idea how to divide my observed items between two factors. Also, I don't think that the 3 factors and 4 factors solutions that I have in mind are nested, which is another problem. I guess that what I ask is what models am I supposed to compare in the preliminary FA? All the papers I am relying on when studying this method decided to go with a onefactor solution, so I never got the chance to see the next step... I am Really grateful for the help 


Why not do an EFA with 14 factors. 


Sounds like a great and simple solution. Didn't know it is acceptable. Thank you! 

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