The data set i use has 214 individuals for which I have different number of observations - varies between 21 and 30. MULTIPLE IMPUTATION IN MPLUS EMPLOYEE DATA •Data set containing scores from 480 employees on eight work-related variables •Variables: •Age, gender, job tenure, IQ, psychological well-being, job satisfaction, job performance, and turnover intentions •33% of the cases have missing well-being scores, and 33% have missing satisfaction scores The FIML approach uses all of the available information in the data and yields unbiased parameter estimates as long as the missingness is at least missing at random. Alternatively, Mplus can create multiply imputed data sets via MCMC simulation. Missing Data Concepts MAR and MCAR. For more details on missing data handling methods, including FIML, see General FAQ: Handling missing or incomplete data and AMOS FAQ: Handling Missing Data using AMOS. Missing data with FIML: Mplus Discussion > Missing Data Modeling > Message/Author Jean-Simon Leclerc posted on Tuesday, June 20, 2017 - 8:09 pm Hi, I started reading about missing data handling techniques in Mplus and I'm not sure to what extent are FIML and MI equivalent for my needs. Structural equation modeling packages, such as Mplus and AMOS, use full information maximum likelihood that is employed seamlessly in a single step when specifying a model. Mplus uses FIML estimation method of missing values that is superior than multiple imputation in most cases. Mplus provides several methods of handling the missing data: listwise deletion, full information maximum likelihood (FIML) and FIML with auxiliary variables. The purpose of the FIML in Lavaan series of posts and the related git repository is to take some of the examples related to FIML estimation within a regression framework from the Applied Missing Data website, and translate them into code for the R package lavaan. Can Mplus handle user missing values (numeric missing values)? Starting with Mplus 5, the default analysis type allows for analysis of missing data by full information maximum likelihood (FIML). Mplus can use multiply imputed data sets that were created by a different software package. Thank you very much. Yes, with the Missing are command. When i estimate this model in Mplus I use dummy variables that load on the observed for the missing data. Unfortunately Mplus doesn't seem to compare models in the context of hierarchical regression at the moment (please let me know if you know a way to do that! Although these missing data approaches have been shown repeatedly to be less biased and Missing Data in Multilevel Regression . I dont recommend to use multiple imputation of data set ıf you want to use CFA. ). I try to estimate a model of nonlinear growth - I specify this using constraints on the factor loadings. I have some general questions regarding my understanding of FIML in MPlus and how to determine which way of dealing with missing data is most appropriate. FIML) lead to worse fit compared to only using valid cases and also data where this was not the case. data, an appropriate, modern method of missing data handling that enables Mplus to make use of all available data points, even for cases with some missing responses. You can give all variables the same missing value, e.g., Missing are all (-999999999) ; You can give different values for different variables, e.g., Missing are x1 x2 (-1) y1 y2 (-5) ; Can you recommend an approach for handling missing data in this case (or point me to resources to help make this decision)? Mplus: full information maximum likelihood (FIML) Stata: may be able to use the -sem- command and hence FIML IVEware: multiple imputation (mi model tied to analysis model) Christine Wells, Ph.D. Imputing missing data in complex survey data 22/ 28 As I understand it, it is not possible to go multi-group analysis with imputed data (and I am not sure imputing data makes sense when 65% of Wave 2 data are missing) and FIML is not possible with WLSMV. I saw datasets where mentioning the variances in the model command (i.e. : I'm a social scientist who recently started using R. Multiple imputation is an option, but I really like how elegantly programs like Mplus handles missing data using FIML.

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