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Article

Подходы к агрегированию результатов множественного заполнения пропусков: сравнительный анализ

 

Multiple imputation is an approach to missing data elimination created by Donald Rubin. The purpose of multiple imputation is to reconstruct the initial structure of data, i.e. to generate the answers as close as possible to hypothetical complete dataset. However, the original algorithm of multiple imputation is complicated and demands a major amount of effort to accomplish. In the study simpler alternative approach averaging of imputed values was experimentally tested against Rubins rule in a number of common research situations. We compared two approaches to multiple imputation results aggregation Rubins rule and averaging of imputed values considering given analytical tools, share of missing values and type of the variable that contains missing values. The results were summed up in a set of recommendations describing a pertinent approach to aggregation for each research situation.