Working With Missing Values

TLDR

Inadequate missing‑value strategies bias estimates, reduce power, and invalidate conclusions. The authors review traditional methods and present single, multiple, and full‑information imputation techniques, providing software results and program examples. Illustrations of missing‑value effects on a linear model and recommendations show that multiple imputation and full‑information methods substantially improve over traditional approaches.

Abstract

Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood estimation. The effects of missing values are illustrated for a linear model, and a series of recommendations is provided. When missing values cannot be avoided, multiple imputation and full information methods offer substantial improvements over traditional approaches. Selected results using SPSS, NORM, Stata (mvis/micombine), and M plus are included as is a table of available software and an appendix with examples of programs for Stata and M plus .