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 Brandon Reed posted on Monday, January 22, 2018 - 4:37 pm
In my time-series data, there are occasional missing data. I am basing my model on example 9.37 (pg. 378).

Should I be using TINTERVAL to handle the missing data (and resulting unequal interval sizes between measurements) OR can my TIME variable be missing values (e.g., 2,5,6,7,8,10).

Thank you in advance,
Brandon
 Bengt O. Muthen posted on Monday, January 22, 2018 - 5:08 pm
Use Tinterval. Time must not have missing data. See also the Short Course Topic 12 video and handout, slide 39 in Parts 3 and 4.
 Jonathan Preszler posted on Monday, January 29, 2018 - 11:21 am
Hi,

I have a question regarding the tinterval command with missingness. Specifically, our tinterval command uses a time variable that is subject-defined (i.e., the time variable indicates how many hours since that subjectís first occasion of measurement). However, this results in a problem for those who have their first occasion of data missing, because the time variable cannot be calculated for any other occasion of measurement for that individual. We thought of two possible solutions to this issue:

1) Subtract the time point for each person with missingness at time 1, making their actual first measurement occasion time 1, even if that occasion was not the first attempt.

2) Take the average time from time 1 to time 2 for those subjects who donít have missingness at time 1, and manually impute that time at time 1 for subjects missing at that time (e.g., if that average elapsed time from t1 to t2 is 3 hours, we subtract 3 hours from t2 for all of those with missingness at t1).

We are leaning towards solution #2, because it seems to presume the least. One important consideration might be the collection method, which is that the occasions of measurement occur at random points within fixed 3-hour time slots across the day.

We were wondering if there is any precedent for this issue or if there is anything we can do?
 Tihomir Asparouhov posted on Monday, January 29, 2018 - 3:51 pm
Unless you are using a cross-classified model, both methods 1 and 2 will be equivalent and should yield identical results. Method 1 is what Mplus does automatically for you, however even if you do Method 2, Mplus automatically removes all missing data rows until it reaches a non-missing observation. Generally we consider the first observed value to be at time t=1. I would recommend looking into the data transformation that Mplus does behind the scenes. Add the command
savedata: file is 1.dat;
and examine the processed data that is being used in the estimation. The DSEM model is stationary and generally it would not suffer from missing first observation.
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