Message/Author 

David Brazel posted on Wednesday, December 13, 2017  4:07 pm



I have intensive longitudinal data measured on a twin sample. I'd like to fit DSEM models to these data but with a three level structure  measurements within individuals within twin pairs. The documentation doesn't appear to have any examples with a three level structure. Is it possible to fit this type of model in MPlus? 


Not yet, but I don't think you need it here. Just let the two twins correspond to bivariate outcomes. 


Hi, I also have an intensive longitudinal data in which participants (level 3) completed up to 7 assessments (level 1) per day for 14 days (level 2). I'd like to use DSEM models to examine the association between X and Y (both measured at level 1) at three levels: momentary, daily, and betweenperson level. (1) Is it possible to fit such a model in Mplus 8.0? (2) Can I use the crossclassified time series analysis(example 9.38) to fit such a model? Thank you! 


You can take a look at http://statmodel.com/download/Aug1718_JH_Slides.zip part 6, page 4853 I would recommend first to do the twolevel model where you have 7*14 times of observations so the cluster variable would be individual with 98 observations in each cluster and using the tinterval command to specify the time of observation. This twolevel model can then be extended to a crossclassified model as in example 9.38. 


Hi, I also have three level data (3 observations per day for 14 days for 117 participants). I would like to use the TINTERVAL command since observation times differed for participants, but with so few observations there will be a high percentage of 'missing' data, which I know is problematic. Should I widen the interval (to every 2 hours instead of 1)? If there are 2 surveys within the 2 hours will one be excluded from the analysis? Or is there a different method better suited to my data? Thank you! 


There are two options I can recommend 1) Use TINTERVAL of 8 hours  that will essentially transform the data so that the 3 observations will be assumed to have been taken 8 hours apart. Observations are not deleted but are spaced out according to the TINTERVAL value. You can then try 4 hours and 2 hours but as you say the amount of missing data may prohibit the estimation. 2) Ignore the timing within day and instead use a factor model F by Y1Y3@1 (&1); for the three within day observations (similar to averaging the 3 observations) See User's guide examples 6.27 and 9.34. 


Thank you for the quick response! I'm trying to model withinday changes, specifically how variables at time 1/2 (4 measures of emotion regulation) affect a variable at time 2/3 (an affect measure), so I'm not sure if the factor model would be appropriate. For TINTERVAL, how would I set up the time variable in my data set? I have the date, time of day, day in the study, survey number, and wave number (1,2,3). Would I have to compute each participant's continuous time spent in the study? 


Take a look at pages 4853 http://www.statmodel.com/download/Part%206%20Asparouhov.pdf That has a within day AR(1) as well as between day AR(1). You can compute the time variable using something like that time=day*24+hour You can do that in Mplus define command. 


Hello again, I tried running it with tinterval, but the PSR's are not very good. I removed one of the random variables, but the PSR's were still not great. I'm not sure how else to improve the model. Do I keep removing random variables? I have also tried pursuing the threelevel model, but am not fully sure that my code is right. Here is what I have: MODEL: %WITHIN% NA2 ON NA1; NA3 ON NA2; ANA2 ON SERQb1; BNA3 ON SERQb2; CNA2 ON SERQc1; DNA3 ON SERQc2; ENA2 ON SERQi1; FNA3 ON SERQi2; GNA2 ON SERQm1; HNA3 ON SERQm2; %BETWEEN% NA1 WITH NA2 NA3 SERQb1 SERQb2 SERQc1 SERQc2 SERQi1 SERQi2 SERQm1 SERQm2; NA2 WITH NA3 SERQb1 SERQb2 SERQc1 SERQc2 SERQi1 SERQi2 SERQm1 SERQm2; NA3 WITH SERQb1 SERQb2 SERQc1 SERQc2 SERQi1 SERQi2 SERQm1 SERQm2; SERQb1 WITH SERQb2 SERQc1SERQc2 SERQi1SERQi2 SERQm1SERQm2; SERQb2 WITH SERQc1SERQc2 SERQi1SERQi2 SERQm1SERQm2; SERQc1 WITH SERQc2 SERQi1 SERQi2 SERQm1 SERQm2; SERQc2 WITH SERQi1 SERQi2 SERQm1 SERQm2; SERQi1 WITH SERQi2 SERQm1 SERQm2; SERQi2 WITH SERQm1 SERQm2; SERQm1 WITH SERQm2; Thanks again! 


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