Missing data are nearly always a problem in research, and missing values represent a
serious threat to the validity of inferences drawn from findings. Increasingly, social science …

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

Multiple imputation was first conceived as a tool that statistical agencies could use to handle
nonresponse in large-sample public use surveys. In the last two decades, the multiple …

Although missing data methods have advanced in recent years, methodologists have
devoted less attention to multilevel data structures where observations at level-1 are nested …

The performance of five simple multiple imputation methods for dealing with missing data
were compared. In addition, random imputation and multivariate normal imputation were …

Multiple imputation is an effective method for dealing with missing data, and it is becoming
increasingly common in many fields. However, the method is still relatively rarely used in …

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 …

Following the seminal publications of Rubin about thirty years ago, statisticians have
become increasingly aware of the inadequacy of “complete-case” analysis of datasets with …

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …

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