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 Harold Chui posted on Tuesday, June 18, 2013 - 11:21 am
Dear Dr. Muthen,

I am planning to use Three-level Multilevel Modeling to look at a study involving therapists, clients, and sessions. I have some missing data - some involving nonresponse to items and some involving noncompletion of entire scales for particular sessions. I plan to let full information maximization likelihood to take care of the missingness, but I first need to calculate total scores from individual items scores.

How should I go about doing this? Should I first conduct some kind of imputation for sessions with missing items to calculate total scores, and then run models using variables involving total scores and choose FIML as the option?

Thanks,
Harold
 Bengt O. Muthen posted on Tuesday, June 18, 2013 - 11:33 am
Why would you use total scores instead of analyzing on the item-level?
 Harold Chui posted on Tuesday, June 18, 2013 - 11:47 am
My predictor (e.g., social support) and outcome (e.g., depression) variables are total-score variables in the MLM.
 Bengt O. Muthen posted on Tuesday, June 18, 2013 - 2:59 pm
Ok, so those 2 constructs are total score variables. Then what is the construct that has individual missing items?
 Harold Chui posted on Tuesday, June 18, 2013 - 4:19 pm
Each total score variable is computed by adding up the scores for individual items. For example, for a scale that has 10 items, I would have 10 "variables." I compute the sum of the 10 scores to create the total score variable. Individual missing items means that I might have 2 missing scores out of the 10, such that I can't compute the total score by summation alone.

Does this mean that I need to use imputation first to fill in the missing items, then compute the total score, before I run models on the total score variables using FIML?
 Bengt O. Muthen posted on Tuesday, June 18, 2013 - 5:05 pm
I am suggesting that you don't create a total score but instead work with the 10 items as indicators of a factor. Missingness is then handled by FIML.
 Corey Savage posted on Thursday, January 28, 2016 - 1:48 am
I am missing data on a number of Rasch scales. Some of these I intend to use as distal outcomes in a latent profile analysis and the others are used as indicators of the latent profiles. Is it appropriate to impute using MI with this type of scale? Thanks!
 Bengt O. Muthen posted on Friday, January 29, 2016 - 6:21 pm
It is a bit dicey to impute when you expect there to be a mixture structure underlying your data because the imputation does not take that into account. You can handle it by "FIML" if you do a 1-step mixture analysis, but if you use a 3-step then the missing on distals will be deleted.
 Corey Savage posted on Friday, January 29, 2016 - 7:42 pm
Would it be appropriate to fit the model first with FIML and allow for lw deletion after once adding covariates and the distal outcome via BCH?
 Bengt O. Muthen posted on Monday, February 01, 2016 - 10:12 am
If you are concerned with missing data I would use 1-step FIML mixture modeling.
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