Fig. 3
From: Evaluation of approaches for multiple imputation of three-level data
Estimated bias in the variance components at level 1, 2 and 3 across the 1000 simulated datasets available case analysis (ACA) and the 8 multiple imputation (MI) approaches under two scenarios for missing data proportions at waves 2, 4 and 6 (10%, 15%, 20% and 20%, 30%, 40%, respectively) and four ICC combinations when data are missing at random (MAR-CATS). The following abbreviations are used to denote different MI methods, e.g., DI: dummy indicators, FCS: fully conditional specification, JM: joint modelling