See pages 7-8 of the user's guide for a brief description of how missing data are handled. It varies by the type of estimator that is used. I believe the Mplus default is the best way to deal with missing data and is preferable to listwise deletion.
Jo Brown posted on Tuesday, November 06, 2012 - 6:18 am
I am using WLSMV so according to the manual, mplus uses pairwise present analysis.
Assuming my data is missing at random,can I use pairwise deletion. I am conscious that there is some debate and I am hoping you could recommend some reading.
How did you get to that link? It's not the link associated with the paper.
Jo Brown posted on Thursday, November 08, 2012 - 5:32 am
Muthén, B., Kaplan, D. & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 3, 431-462
and the first search hit directs me to the link above which contains a list of papers on missing data including the paper you recommended.
Anyway, the link you sent me worked - thank you again!
Cecily Na posted on Wednesday, August 14, 2013 - 11:42 am
Dear Professor, I have four dummy variables as predictors and 20+ outcome variables. I did not put all the outcome variables in a single model, but organized them into separate groups with each group containing more related outcome variables. Eventually I have four or five models, each with four or five outcome variables and the same four dummy variables as predictors.
My question is I used the same sample for all these models, but the model results showed different sample sizes across these models. What is the reason? As default, Mplus only use FIML for the missing outcome values, not for the predictors (exogenous variables). So as long as predictors and sample data are the same, the sample size should be the same. Thank you in advance for clarification.
una posted on Wednesday, November 13, 2013 - 5:44 am
Dear Prof. Muthen,
In my analysis with MLR, the dependent variable is a latent variable, while the independent variables are observed. I have 498 respondents. I am not specifying “listwise is on”. However, in the final analysis I only keep 309, with the warning “Data set contains cases with missing on x-variables. These cases were not included in the analysis. Number of cases with missing on x-variables: 189”.
In this regard, I have two questions:
1.Is it correct to state: “Missing data on the dependent variables were treated with full information maximum likelihood (FIML). Missing data on independent variables were treated using listwise deletion”
In regression, the model is estimated conditioned on the observed exogenous variables. Missing data theory applies to only endogenous dependent variables. If you mention the variances of all of your independent variables in the MODEL command, they will be treated as dependent variables and distributional assumptions will be made about them but they will not be excluded from the analysis.
I am running a SEM cross-lagged model in MPlus, in which I include 12 teachers and their 226 students.
All 226 students have reported on their behavior and their relationship with the teacher. The 12 teachers have each reported on the behavior of and relationship with 12 of their students, resulting in 144 teacher reports.
However, when I include all 226 students and 12 teachers in the analyses, MPlus still states that the N is 226 and not 144 (while for 82 students, there are no teacher-reports available). What does MPlus do with these 82 students? How does it use their info? Or should I perhaps exclude these students in my analyses by using LISTWISE=ON?