How does Mplus handle missing data?
Mplus does not do imputations, but handles missing data in a general way using ML under MAR. Mplus can handle missing on x’s if they are brought into the model as “y’s”. This is done automatically in some tracks of the program (such as non-mixture, non-categorical).
Why is Mplus excluding cases with missing values when the model does not specify Listwise deletion?
Why is Mplus excluding cases with missing values when the model does not specify listwise deletion? | Mplus FAQ. Mplus can be used to estimate a model in which some of the variables have missing values using full information maximum likelihood (FIML).
What is Mplus?
Mplus is a highly flexible, powerful statistical analysis software program that can fit an extensive variety of statistical models using one of many estimators available. Perhaps its greatest strengths are in its capabilities to model latent variables, both continuous and categorical, which underlie its flexibility.
What is multiple imputation for missing data?
Multiple imputation is a general approach to the problem of missing data that is available in several commonly used statistical packages. It aims to allow for the uncertainty about the missing data by creating several different plausible imputed data sets and appropriately combining results obtained from each of them.
What is FIML in Mplus?
Mplus can be used to estimate a model in which some of the variables have missing values using full information maximum likelihood (FIML).
How is Mplus used?
Mplus is a powerful statistical package used for the analysis of latent variables. Among the kinds of analysis it can perform are exploratory factor analysis, confirmatory factor analysis, latent class analysis, latent growth curve modeling, structural equation modeling and multilevel modeling.
What is the default estimator in Mplus?
WLSMV estimator
By default, Mplus uses WLSMV estimator for both structural and measurement part.
When Should multiple imputation be used?
Multiple imputation has been shown to be a valid general method for handling missing data in randomised clinical trials, and this method is available for most types of data [4, 18,19,20,21,22].
How do you impute missing data?
Another common approach among those who are paying attention is imputation. Imputation simply means replacing the missing values with an estimate, then analyzing the full data set as if the imputed values were actual observed values.
Is multiple imputation necessary?
Predictor variables must not be imputed. Multiple imputation must not be used because you will end up with several different outcomes of your statistical analysis.
How much imputation is too much?
Statistical guidance articles have stated that bias is likely in analyses with more than 10% missingness and that if more than 40% data are missing in important variables then results should only be considered as hypothesis generating [18], [19].