REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types. 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 reflected not only in the model of interest, but also in the imputation model. In particular, the imputation model should reflect the differences 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.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
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- Quartagno, Matteo; Carpenter, James R.: Multiple imputation for discrete data: evaluation of the joint latent normal model (2019)
- Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
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- Lago, Luise Patricia; Clark, Robert Graham: Imputation of household survey data using linear mixed models (2015)
- Carpenter, James; Kenward, Michael: Multiple imputation and its applications (2013)
- George Leckie; Chris Charlton: runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata (2013) not zbMATH
- James Carpenter; Harvey Goldstein; Michael Kenward: REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types (2011) not zbMATH
- Recai Yucel: State of the Multiple Imputation Software (2011) not zbMATH