Multivariate imputation by chained equations (MICE) has emerged as a principled method of
dealing with missing data. Despite properties that make MICE particularly useful for large …

Following its introduction nearly 30 years ago (Rubin, 1978), multiple imputation (MI) has
become an important and influential approach for dealing with the statistical analysis of …

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies
provides a comprehensive introduction to the multiple imputation approach to missing data …

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 …

Educational researchers have become increasingly aware of the problems and biases
which can be caused by missing data. Significant advances have been made in the last 15 …

Although social scientists devote considerable effort to mitigating measurement error during
data collection, they often ignore the issue during data analysis. And although many …

The treatment of missing data can be difficult in multilevel research because state-of-the-art
procedures such as multiple imputation (MI) may require advanced statistical knowledge or …

Background: Multiple imputation is often used to reduce bias and gain efficiency when there
is missing data. The most appropriate imputation method depends on the model the analyst …

Sample estimates derived from data with missing values may be unreliable and may
negatively impact the inferences that researchers make about the underlying population due …

We consider the relative performance of two common approaches to multiple imputation
(MI): joint MI, in which the data are modeled as a sample from a joint distribution; and …