Multiple Imputation for Missing Data: Making the Most of What You Know


Journal Article (Review)

Missing data are a common problem in organizational research. Missing data can occur due to attrition in a longitudinal study or nonresponse to questionnaire items in a laboratory or field setting. Improper treatments of missing data (e.g., listwise deletion, mean imputation) can lead to biased statistical inference using complete case analysis statistical techniques. This article presents a simulation and data analysis case study using a method for dealing with missing data, multiple imputation, that allows for valid statistical inference with complete case statistical analysis. Software for implementing multiple imputation under a multivariate normal model is freely and widely available (e.g., NORM, SAS, SOLAS). It should be routinely considered for imputing missing data. The authors illustrate the application of this technique using data from the HomeNet project.

Full Text

Duke Authors

Cited Authors

  • Fichman, M; Cummings, JN

Published Date

  • January 1, 2003

Published In

Volume / Issue

  • 6 / 3

Start / End Page

  • 282 - 308

International Standard Serial Number (ISSN)

  • 1094-4281

Digital Object Identifier (DOI)

  • 10.1177/1094428103255532

Citation Source

  • Scopus