Elina Vaara posted on Monday, November 11, 2013 - 12:03 am
I have longitudinal NMAR data (n in the first round about 2500). There is severe (about 30%) dropout rate from 1st measurement timepoint, and I wish to separate three agegroups and do analysis for them separately. I have earlier done CFA with categorical variables, but it does listwise deletion.
I have 30 correlated variables, which I plan to use in longitudinal BSEM with Mplus, and check for measurement invariance longitudinally and between genders. I am thinking is this a good way to do my analysis: even if Bayes estimation gives me more flexibility, I was wondering if the amount of missing data is still a concern?
I have found papers on Bayes and papers on missing data, but still I am not convinced that I could rely on BSEM results with nonignorable missingness I have. Could you give me advise on this?
Bayes and maximum likelihood both require MAR and have the same concerns. For NMAR, see the following paper which is available on the website:
Muthén, B., Asparouhov, T., Hunter, A. & Leuchter, A. (2011). Growth modeling with non-ignorable dropout: Alternative analyses of the STAR*D antidepressant trial. Psychological Methods, 16, 17-33.
Elina Vaara posted on Sunday, November 17, 2013 - 11:45 pm
Thank you! I have your article now, but here are a few more questions: For now I should see how the latent factors are associated in time. I wish to see change/association of different timepoints for about 5 factors (from CFA or such for ordinal variables). Growth curves model just one aspect at a time? So should I use (continous) latent factors as outcomes in five separate growth analyses? I would see possibility to model dropout (ex. pattern mixture approeach) and 5 longitudinal factors at the same time.
MODEL: f1 by Evoc* Reco ERey; f2 by CRey* OriL; f3 by Sem* Nome Leit Esc; f4 by Aprend* Flue Tril; f1-f4 ON Age Educat (reg1-reg8);
f1(f1var); f2(f2var); f3(f3var); f4(f4var); Age(idvar); Educat(escvar); [Age](id); [Educat](es); Age with Educat(idesc);
Aprend with Flue-Tril(res1-res11); Flue with Evoc-Tril(res12-res21); Evoc with Reco-Tril(res22-res30); Nome with Sem-Tril(res31-res37); Sem with CRey-Tril(res38-res43); CRey with OriL-Tril(res44-res48); OriL with ERey-Tril(res49-res52); ERey with Esc-Tril (res53-res54); Leit with Esc-Tril (res56-res57); Esc with Tril (res58);
MODEL PRIORS: res1-res58 ~N(0, 0.01); f1var-f4var ~IG(0.001,0.001); reg1-reg8 ~N(0, 0.01); idvar ~IG(0.001,0.001); escvar ~IG(0.001,0.001); id ~N(0,100000); es ~N(0,100000); idesc ~N(0, 0.01);
Thanks Dr. Linda! I am sorry I was not clear enough: my doubts are about the MODEL PRIORS. My data fits well in frequentist analysis (SEM), but not in Bayesian (BSEM), maybe because PRIORS mistakes. It runs, but PPpvalue is about 0.01. I would like to use Bayesian because the small sample. I tried to use OpenBUGS software, but I think MPlus is much easier (however, Bayes analysis is more complicated to me). May I use the svalues obtained in OUTPUT command as PRIORS? Are they more informative that those ones I posted? Thanks for your attention!
If you are a beginner Bayesian, I would go by the Mplus defaults and not give priors. Setting such specific priors is not for the beginner Bayesian. If you haven't already, you should study my paper on our website
Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report. Version 3. Click here to view Mplus inputs, data, and outputs used in this paper.
and also carefully look at the Mplus scripts for that paper as well as for the paper
Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335. Click ""download paper"" below for the latest version of October 21, 2011. Download the 2nd version dated April 14, 2011. Click here to view the seven web tables referred to in the paper and here to view Mplus inputs, data, and outputs used in this version of paper. Download the 1st version dated September 29, 2010 containing a MIMIC section and more tables, and the corresponding Mplus inputs, data, and outputs here. The seven web tables correspond to tables 8, 10, 17, 18, 19, 20, and 21 of the first version.
Note also that priors need to be chosen in relation to the scale of the variables.