Realcom Imputation

Realcom logoSpecially written REALCOM macros with a menu interface are now available for handling quite general missing data patterns. These will deal properly with categorical as well as normal data and also with multilevel structures. The model of interest is first set up in MLwiN in the usual way and then the variables exported to REALCOM-Impute and then the imputed datasets returned to MLwiN where they will be fitted and combined automatically for the specified model of interest.


Previous Bugs (earlier versions of Realcom-Imputation)


To resolve the following bugs please ensure you have the latest version of REALCOM-Impute


Missing data


Missing data are a persistent problem in social and other datasets. A standard technique for handling missing values efficiently is known as multiple imputation and the software REALCOM-Impute is unique in that it has been designed to implement this procedure for 2-level data. Apart from being able to deal with 2-level data it can also handle properly categorical data, whether in the response or predictor variables in a model. An interface is provided with MLwiN that allows users to carry out the full procedure and fit their final model semi-automatically.

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