Mplus Users Guide v6.. - Muthén & Muthén

CHAPTER 14Multiple data sets generated using multiple imputation (Rubin, 1987;Schafer, 1997) can be analyzed using a special feature of Mplus.Parameter estimates are averaged over the set of analyses, and standarderrors are computed using the average of the standard errors over the setof analyses and the between analysis parameter estimate variation.In all models, missingness is not allowed for the observed covariatesbecause they are not part of the model. The model is estimatedconditional on the covariates and no distributional assumptions are madeabout the covariates. Covariate missingness can be modeled if thecovariates are brought into the model and distributional assumptionssuch as normality are made about them. With missing data, the standarderrors for the parameter estimates are computed using the observedinformation matrix (Kenward & Molenberghs, 1998). Bootstrapstandard errors and confidence intervals are also available with missingdata.With missing data, it is useful to do a descriptive analysis to study thepercentage of missing data as a first step. This can be accomplished byspecifying TYPE=BASIC in the ANALYSIS command. The output forthis analysis produces the number of missing data patterns and theproportion of non-missing data, or coverage, for variables and pairs ofvariables. A default of .10 is used as the minimum coverage proportionfor a model to be estimated. This minimum value can be changed byusing the COVERAGE option of the ANALYSIS command.DATA MISSING BY DESIGNData missing by design occurs when the study determines which subjectswill be observed on which measures. One example is when differentforms of a measurement instrument are administered to randomlyselected subgroups of individuals. A second example is when it isexpensive to collect data on all variables for all individuals and only asubset of variables is measured for a random subgroup of individuals. Athird example is multiple cohort analysis where individuals who aremeasured repeatedly over time represent different birth cohorts. Thesetypes of studies can use the missing data method where all individualsare used in the analysis, including those who have missing values onsome of the analysis variables by design. This type of analysis isobtained by identifying the values in the data set that are considered to436