Multiple imputation (MI) is an approach for handling missing values in a data set that allows
researchers to use the entirety of the observed data. Although MI has become more …

Background Multiple imputation is often used for missing data. When a model contains as
covariates more than one function of a variable, it is not obvious how best to impute missing …

Missing data in public health research is a major problem. Mean or median imputation is
frequently used because it is easy to implement. Although multiple imputation has good …

Methods for handling missing data in social science data sets are reviewed. Limitations of
common practical approaches, including complete-case analysis, available-case analysis …

The theory of multiple imputation for missing data requires that imputations be made
conditional on the sampling design. However, most standard software packages for …

A great deal of recent methodological research has focused on two modern missing data
analysis methods: maximum likelihood and multiple imputation. These approaches are …

Background Multiple imputation is becoming increasingly popular for handling missing data.
However, it is often implemented without adequate consideration of whether it offers any …

Two classes of modern missing data procedures, maximum likelihood (ML) and multiple
imputation (MI), tend to yield similar results when implemented in comparable ways. In either …

Amelia II is a complete R package for multiple imputation of missing data. The package
implements a new expectation-maximization with bootstrapping algorithm that works faster …