The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data

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Abstract
A Monte Carlo simulation examined the performance of a recently available full information maximum likelihood (FIML) estimator in a multiple regression model with missing data. The effects of four independent variables were examined (missing data technique, missing data rate, sample size, and correlation magnitude) on three outcome measures: regression coefficient bias, R2 bias, and regression coefficient sampling variability. Three missing data patterns were examined based on Rubin’s missing data theory: missing completely at random, missing at random, and a nonrandom pattern. Results indicated that FIML estimation was superior to the three ad hoc techniques (listwise deletion, pairwise deletion, and mean imputation) across the conditions studied. FIML parameter estimates generally had less bias and less sampling variability than the three ad hoc methods.