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 …

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 …

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 …

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 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 …

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 …

Missing data frequently complicates data analysis for scientific investigations. The
development of statistical methods to address missing data has been an active area of …

The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …

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 …

This chapter describes a general approach to handling missing data in psychological
research. It provides a theoretical background in readable, nontechnical fashion. Our overall …