Including Auxiliary Variables in Models with Missing Data Using Full Information Maximum Likelihood
Stata’s sem command includes the ability to estimate models with missing data using full information maximum likelihood estimation (FIML). One of the assumptions of FIML is that the data is at least missing at random (MAR), that is, conditional on other variables in the model, missingness is not dependent on the value that would have been observed. The MAR assumption can be made more plausible and estimation improved by the inclusion of auxiliary variables, that is, variables that predict missingness or are related to the variables with missing values, but are not part of the substantive model. The inclusion of auxiliary variables is common in multiple imputation models, but less common in models estimated using FIML. This presentation will introduce users the saturated correlates model (Graham 2003), a method of including auxiliary variables in FIML models. Examples demonstrating how to include auxiliary variables using the saturated correlates model with Stata’s sem command will be shown.
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