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

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 …

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 …

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 …

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 …

Statistical procedures for missing data have vastly improved, yet misconception and
unsound practice still abound. The authors frame the missing-data problem, review …

A great deal of recent methodological research has focused on two modern missing data
analysis methods: maximum likelihood and multiple imputation. These approaches are …

This article compares two missing data procedures, full information maximum likelihood
(FIML) and multiple imputation (MI), to investigate their relative performances in relation to …

Recent work suggests that multiple imputation and specific modeling techniques offer
general methods for dealing with missing data that perform well across many types of …

The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …