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Table 2 Summary of the imputation approaches for handling incomplete three-level data

From: Evaluation of approaches for multiple imputation of three-level data

MI approach Paradigm Model Softwarea How the two sources of clustering are handled
Clustering due to higher level clusters Clustering due to repeated measures
JM-1L-DI-wide JM Standard (single-level) SAS [64], SPSS [36], Stata [35], Mplus [24], R [46] DI Repeated measures arranged in wide format
FCS-1L-DI-wide FCS Standard (single-level) SAS, SPSS, Stata, Mplus, R, Blimp [26] DI Repeated measures arranged in wide format
JM-2L-wide JM Two-level MLMM SAS [28], Mplus, Realcom-impute [23], Stat-JR [29], R RE Repeated measures arranged in wide format
JM-2L-wide DI RE
FCS-2L-wide FCS Two-level LMM Mplus, R, Blimp RE Repeated measures arranged in wide format
FCS-2L-DI DI RE
JM-3L JM Three-level MLMM Stat-JR, Mplus RE RE
CS-3L FCS Three-level LMM R, Blimp RE RE
  1. DI dummy indicators, FCS fully conditional specification, JM joint modelling, LMM linear mixed model, MLMM multivariate linear mixed model, RE random effects
  2. aR and Blimp are the only freely available, open-source software implementations