In an LCA analysis I am currently implementing in Mplus, there are some missing data points. In my code I have simply written: MISSING ARE ALL (999); 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.
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.