Multiple Imputation of Missing Data in Cross-Cultural Samples

• Published in: Cross-cultural research Vol. 43; no. 3; pp. 206 - 229
• Main Authors: Dow, Malcolm M., Eff, E. Anthon
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.08.2009; Sage Publications; SAGE PUBLICATIONS, INC
• Subjects: Cross cultural studies; Cross-cultural analysis; Crosscultural Differences; Data analysis; Data collection; Estimation bias; History, theory and methodology; Methodology; Sociology; Statistical analysis; Statisticians; Rubin's rules; multiple imputation; multiple imputation by chained equations (MICE); missing data; missingness assumptions; Statistical analysis; Methodology; Crossnational Comparisons; Sampling
• ISSN: 1069-3971; 1552-3578
• DOI: 10.1177/1069397109333362

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.