Multiple Imputation as a Solution to the Missing Data Problem in Social Sciences
Multiple Imputation as a Solution to the Missing Data Problem in Social Sciences

Summary/Abstract: In this paper I analyze a series of techniques designed for replacing missing data. From the extensive literature on political values in postcommunist countries, I selected one of the most discussed models – the one proposed by Reisinger et al. (1994). In analyzing political values in Russia at the beginning of the transition, their model represents a significant contribution. The main disadvantage of the analyses of this model, however, is given by the substandard treatment of the missing data: listwise deletion. Since statistical theory suggests alternative techniques that offer unbiased estimators, in this paper I replicate the model using three different methods (mean imputation, regressionbased imputation, and multiple imputation) to test the robustness of its findings. The results of this replication show that the initial findings are not robust and indicate the multiple imputation method as a solution for obtaining unbiased estimators in the presence of missing data.

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