Multiple imputation provides a useful strategy for dealing with data sets with missing values.
Instead of filling in a single value for each missing value, Rubin's (1987) multiple imputation …

Multiple imputation provides a useful strategy for dealing with data sets that have missing
values. Instead of filling in a single value for each missing value, a multiple imputation …

Statistical analysis with missing data has always been a challenge. However, there have
been tremendous advances in statistical theory related to analysis with missing data. Of …

Gary King, James Honaker, Anne Joseph, and Kenneth Scheve.“Analyzing Incomplete
Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political …

This article provides a comprehensive review of multiple imputation (MI), a technique for
analyzing data sets with missing values. Formally, MI is the process of replacing each …

This chapter offers an accessible introduction to missing-data procedures, especially for
longitudinal data, using multiple imputation (Rubin, 1987; Shafer, 1997a), which is the …

The method of multiple imputation (MI) is used increasingly for analyzing datasets with
missing observations. Two sets of tasks are required in order to implement the method:(a) …

Analyses of multivariate data are frequently hampered by missing values. Until recently, the
only missing-data methods available to most data analysts have been relatively ad1 hoc …

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 …

Multiple imputation: a primer - Joseph L Schafer, 1999 Skip to main content Intended for
healthcare professionals Sage Journals Home Search this journal Search all journals Enter …