New Stat-JR functionality to support analyses of incomplete datasets.
There are now three principal Stat-JR templates available to support handling missing data in multilevel generalised linear models; these use two different approaches.
The first two templates use ‘multiple imputation’ which is a widely used procedure that will handle a large number of models: a 2-level (2LevelImpute; available since 2014) and a new N-level (NLevelImpute) version are now available.
The second (one pass) approach is a more recent generalisation with a more robust theoretical justification (see Goldstein et al, 2014 for further details); this has been implemented in the new 2LevelMissingOnePass template.
Goldstein, H., Carpenter, J. R. and Browne, W. J. (2014), Fitting multilevel multivariate models with missing data in responses and covariates that may include interactions and non-linear terms. Journal of the Royal Statistical Society: Series A (Statistics in Society). 177(2), 553-564 doi: 10.1111/rssa.12022