Ck. Enders, The performance of the full information maximum likelihood estimator in multiple regression models with missing data, EDUC PSYC M, 61(5), 2001, pp. 713-740
A Monte Carlo simulation examined the performance of a recently available f
ull information maximum likelihood (FIML) estimator in a multiple regressio
n model with missing data. The effects of four independent variables were e
xamined (missing data technique, missing data rate, sample size, and correl
ation magnitude) on three outcome measures regression coefficient bias, R-2
bias, and regression coefficient sampling variability. Three missing data
patterns were examined based on Rubin's missing data theory: missing comple
tely at random, missing at random, and a nonrandom. pattern. Results indica
ted that FIML estimation was superior to the three ad hoc techniques (listw
ise deletion, pairwise deletion, and mean imputatiom) across the conditions
studied, FM parameter estimates generally had less bias and less sampling
variability than the three ad hoc methods.