Default Missing Data for LCA PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
 Benjamin Goodman posted on Monday, September 17, 2018 - 9:52 am
In an LCA analysis I am currently implementing in Mplus, there are some missing data points. In my code I have simply written:
since all of my missing values have been assigned as 999.

The analysis is TYPE=MIXTURE, and I am also implementing a covariate analysis.

My question is, what is Mplus doing with the missing data? I know it isn't deleting it, so it must be using some form of imputation; I have run the code with missing data points deleted and my results change. I would like to know what imputation method is being implemented, or at least find some reference to it in terms of a command in Mplus. Is there like a default setting which can be changed? I would figure it doesn't implement the same methods as CFA.

Any help would be really appreciated. Thank you for your time.
 Bengt O. Muthen posted on Monday, September 17, 2018 - 1:59 pm
ML does not need to use imputation of missing values but directly computes the parameter estimates using all available information, taking the missingness into account under the assumption of "MAR"- this is often called FIML. See any missing data book such as the ones by Little & Rubin, Enders, or Chapter 10 of Muthen, Muthen & Asparouhov. All models that use ML use this same general technique.
 Benjamin Goodman posted on Tuesday, September 18, 2018 - 5:55 am
Thank you, this is very helpful.
 Benjamin Goodman posted on Wednesday, September 19, 2018 - 10:13 am
For anyone looking for a quick reference for ML (maximum likelihood) and it's relationship to MI (multiple imputation), I found the following reference to be very helpful:

This is just in case anyone else in the future happens upon this thread and is searching for a reference. It discusses the formal differences between MAR, MCAR, and NMAR as well which I found helpful.
 Bengt O. Muthen posted on Wednesday, September 19, 2018 - 10:29 am
And if you want an Mplus-specific discussion of these issues including scripts for a variety of missing data models, again, see Chapter 10 of our RMA book:

Muthen, Muthen, Asparouhov (2017). Regression and Mediation Analysis using Mplus.
Back to top
Add Your Message Here
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Options: Enable HTML code in message
Automatically activate URLs in message