This Article Full Text (PDF) All Versions of this Article: 1069397109333362v1 43/3/206    most recent References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Dow, M. M. Articles by Eff, E. A. Search for Related Content Social Bookmarking                         What's this?

Multiple Imputation of Missing Data in Cross-Cultural Samples

Northwestern University, mmd383{at}northwestern.edu

E. Anthon Eff

Middle Tennessee State University

Listwise deletion of cases with missing data prior to statistical analysis, the approach overwhelmingly used by cross-cultural survey researchers, requires the assumption that the missing data are missing completely at random. This assumption is not often likely to hold for cross-cultural sample data, and when it fails statistical analysis based only on complete-case subsamples introduces the possibility of biased estimates and standard errors. Over the past 20 or so years statisticians have made major advances in specifying the conditions under which missing data can be ignored when making inferences based on incomplete data. We review these conditions since they have a direct bearing on when the usual approaches to dealing with missing cross-cultural survey data are invalid.

Key Words: missing data • multiple imputation • missingness assumptions • Rubin's rules • multiple imputation by chained equations (MICE)

This version was published on August 1, 2009

Cross-Cultural Research, Vol. 43, No. 3, 206-229 (2009)
DOI: 10.1177/1069397109333362


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?