Abstract
Multiple imputation is becoming increasingly established as the leading practical approach to modelling partially observed data, under the assumption that the data are missing at random. However, many medical and social datasets are multilevel, and this structure should be reected not only in the model of interest, but also in the imputation model. In particular, the imputation model should re
ect the dierences between level 1 variables and level 2 variables (which are constant across level 1 units). This led us to develop the REALCOM-IMPUTE software, which we describe in this article. This software performs multilevel multiple imputation, and handles ordinal and unordered categorical
data appropriately. It is freely available on-line, and may be used either as a standalone package, or in conjunction with the multilevel software MLwiN or Stata.
ect the dierences between level 1 variables and level 2 variables (which are constant across level 1 units). This led us to develop the REALCOM-IMPUTE software, which we describe in this article. This software performs multilevel multiple imputation, and handles ordinal and unordered categorical
data appropriately. It is freely available on-line, and may be used either as a standalone package, or in conjunction with the multilevel software MLwiN or Stata.
| Original language | English |
|---|---|
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | Journal of Statistical Software |
| Volume | 45 |
| Issue number | 5 |
| Publication status | Published - 2011 |
Keywords
- multilevel multiple imputation, missing data, mixed response types.