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).
#Mplus #statistics #SPSS #missingdata #mplusforbeginners #sem #cfa #regressionanalysis #fiml #mar #mcar #geiser #quantfish
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[@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