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 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?
 Bengt O. Muthen posted on Wednesday, December 13, 2017 - 4:34 pm
Not yet, but I don't think you need it here. Just let the two twins correspond to bivariate outcomes.
 Ruixue, Z.Y. posted on Thursday, March 22, 2018 - 3:26 pm
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 between-person level.
(1) Is it possible to fit such a model in Mplus 8.0?

(2) Can I use the cross-classified time series analysis(example 9.38) to fit such a model?

Thank you!
 Tihomir Asparouhov posted on Friday, March 23, 2018 - 9:49 am
You can take a look at
http://statmodel.com/download/Aug17-18_JH_Slides.zip
part 6, page 48-53

I would recommend first to do the two-level 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 two-level model can then be extended to a cross-classified model as in example 9.38.
 Lisa Vittorio posted on Thursday, March 26, 2020 - 11:30 am
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!
 Tihomir Asparouhov posted on Thursday, March 26, 2020 - 1:06 pm
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 Y1-Y3@1 (&1);
for the three within day observations (similar to averaging the 3 observations)
See User's guide examples 6.27 and 9.34.
 Lisa Vittorio posted on Thursday, March 26, 2020 - 2:50 pm
Thank you for the quick response! I'm trying to model within-day 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?
 Tihomir Asparouhov posted on Thursday, March 26, 2020 - 3:38 pm
Take a look at pages 48-53
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.
 Lisa Vittorio posted on Tuesday, March 31, 2020 - 8:08 am
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 three-level model, but am not fully sure that my code is right. Here is what I have:

MODEL:
%WITHIN%
NA2 ON NA1;
NA3 ON NA2;

A|NA2 ON SERQb1;
B|NA3 ON SERQb2;

C|NA2 ON SERQc1;
D|NA3 ON SERQc2;

E|NA2 ON SERQi1;
F|NA3 ON SERQi2;

G|NA2 ON SERQm1;
H|NA3 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 SERQc1-SERQc2 SERQi1-SERQi2 SERQm1-SERQm2;
SERQb2 WITH SERQc1-SERQc2 SERQi1-SERQi2 SERQm1-SERQm2;
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!
 Bengt O. Muthen posted on Tuesday, March 31, 2020 - 9:41 am
It is easier for us to help you if you send your output - and data if possible - to Support@statmodel.com along with your license number.
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