ABSTRACT In Ordinary Least Square regression, researchers often are interested in
knowing whether a set of parameters is different from zero. With complete data, this could be …

Missing data are frequently encountered in practice. A broader class of missing data is
called incomplete data, which includes data with measurement error, multilevel data with …

Most current implementations of multiple imputation (MI) assume that data are missing at
random (MAR), but this assumption is generally untestable. We performed analyses to test …

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple
times and the resulting estimates of the parameter (s) of interest are combined across the …

Applications of multiple imputation have long outgrown the traditional context of dealing with
item nonresponse in cross-sectional datasets. Nowadays multiple imputation is also applied …

Multiple imputation for nonresponse in public-use files replaces each missing value by two
or more plausible values. The values can be chosen to represent both uncertainty about …

Multilevel data are often incomplete, for example, when participants refuse to answer some
items in a questionnaire or drop out of a study that involves multiple measurement …

In the last decade, substantial progress has been made on methods for imputation of
missing data. Modern imputation methods have become widely available for practitioners …

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

Multiple imputation (MI) is a well-established method to handle item-nonresponse in sample
surveys. Survey data obtained from complex sampling designs often involve features that …