FIML Missing Data Handling in Mplus

QuantFish instructor Dr. Christian Geiser shows how to handle missing data in Mplus using full information maximum likelihood (FIML).
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[@crazystatistician]

Many thanks for this great video, Dr. Geiser! I have a basic question. To use FIML in Mplus, I've applied the following command: MISSING ARE ALL (-9); ANALYSIS: ESTIMATOR = ML; I understand that without specifying the estimator, Mplus uses ML by default when the data is normally distributed. However, I have not included the command related to auxiliary variables that you mentioned in the video. In this case (only using the command above), can I still use FIML as a technique for handling missing data? By the way, I've checked the number of observations in Mplus and this number is the same with the number of respondents in SPSS. Thank you so much in advance for your answer.

[@tessawashburn1552]

Thank you, Dr. Geiser! @QuantFish Can you list the exogenous variables' variances as parameters when using WLSMV to get Mplus to estimate more of the missing data? Or is this not possible since WLSMV uses a partially pairwise deletion estimation? In other words, is listing the exogenous variables' variances as parameters only possible when using ML and MLR estimation methods? Thank you for all your videos. They are very helpful and informative!

[@shiranlipetz3382]

Thanks! Is it possible to use FIML for dichotomous variables? Some of my variables are independent and others are dependent

[@DrFurb]

Thank you. Might I ask this missingness technique is only available for regression models (not things like mixture modelling)?

[@QuantFish]

FIML is also available for mixture models. Best, Christian Geiser