Multiple Imputation of Missing Data in Cross-Cultural Samples  

Oleh:

Dow, Malcolm M. ; Eff, E. Anthon

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Cross-Cultural Research vol. 43 no. 03 (Aug. 2009), page 206-229.
Topik: missing data; multiple imputation; missingness assumptions; Rubin’s rules; multiple imputation by chained equations (MICE)
Fulltext: 206.pdf (319.78KB)

Isi artikel

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.

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