Using Multiple Imputation for Vote Choice Data: A Comparison across Multiple Imputation Tools


One commonly acknowledged challenge in polls or surveys is item non-response, i.e., a significant proportion of respondents conceal their preferences about particular questions. This paper applies the multiple imputation (MI) method to reconstruct the distribution of vote choice in the sample. Vote choice is one of most important dependent variables in political science studies. This paper shows how the MI procedure in general facilitates the work of reconstructing the distribution of a targeted variable. Particularly, it shows how MI can be applied to point-estimation in descriptive statistics. The three packages of R, AmeliaII, MICE, and mi, are employed for this project. The findings, based on a Taiwan Election and Democratization Study (TEDS) samples collected after the 2012 presidential election (N = 1826) suggest the following: First, there is little adjustment done given the MI methods; Second, the three tools based on two algorithms lead to similar results, while Amelia II and MICE perform better. Although the results are not striking, the implications of these findings are worthy of discussion.

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Liu, F. (2014) Using Multiple Imputation for Vote Choice Data: A Comparison across Multiple Imputation Tools. Open Journal of Political Science, 4, 39-46. doi: 10.4236/ojps.2014.42006.

Conflicts of Interest

The authors declare no conflicts of interest.


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