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

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DOI: 10.4236/ojps.2014.42006    4,676 Downloads   6,437 Views   Citations
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ABSTRACT

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

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.

References

[1] Allison, P. D. (2001). Missing Data. London: Sage Publications, Inc.
[2] Barzi, F. (2004). Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies. American Journal of Epidemiology, 160, 34-45.
http://dx.doi.org/10.1093/aje/kwh175
[3] Bernaards, C. A., Farmer, M. M., Qi, K., Dulai, G. S., Ganz, P. A., & Kahn, K. L. (2003). Comparison of Two Multiple Imputation Procedures in a Cancer Screening Survey. Journal of Data Science, 1, 293-312.
[4] Bernhagen, P., & Marsh, M. (2007). The Partisan Effects of Low Turnout: Analyzing Vote Abstention as a Missing Data Problem. Electoral Studies, 26, 548-560.
http://dx.doi.org/10.1016/j.electstud.2006.10.002
[5] Florez-Lopez, R. (2010). Effects of Missing Data in Credit Risk Scoring. A Comparative Analysis of Methods to Achieve Robustness in the Absence of Sufficient Data. Journal of the Operational Research Society, 61, 486-501.
http://dx.doi.org/10.1057/jors.2009.66
[6] Gelman, A., King, G., & Liu, C. (1998). Not Asked and Not Answered: Multiple Imputation for Multiple Surveys. Journal of the American Statistical Association, 93, 846-857.
http://dx.doi.org/10.1080/01621459.1998.10473737
[7] Graham, J. W. (2009). Missing Data Analysis: Making It Work in the Real World. Annual Review of Psychology, 60, 549576.
http://dx.doi.org/10.1146/annurev.psych.58.110405.085530
[8] He, Y., & Raghunathan, T. E. (2009). On the Performance of Sequential Regression Multiple Imputation Methods with NonNormal Error Distributions. Communications in Statistics-Simulation and Computation, 38, 856-883.
http://dx.doi.org/10.1080/03610910802677191
[9] Honaker, J., King, G., & Blackwell, M. (2009). Amelia Software Web Site
http://gking.harvard.edu/amelia
[10] Honaker, J., King, G., & Blackwell, M. (2011). Amelia II: A Program for Missing Data. Journal of Statistical Software, 45, 1-47.
[11] Imai, K., King, G., & Lau, O. (2004). Zelig: Everyone’s Statistical Software.
http://GKing.Harvard.Edu/zelig
[12] King, G., Honaker, J., Joseph, A., & Scheve, K. (2001). Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation. American Political Science Review, 95, 49-69.
[13] Liu, F. C.-S. (2010). Reconstruct Partisan Support Distribution with Multiply Imputed Survey Data: A Case Study of Taiwan’s 2008 Presidential Election. Survey Research, 24, 135-162.
[14] Paul, C., Mason, W. M., McCaffrey, D., & Fox, S. A. (2008). A Cautionary Case Study of Approaches to the Treatment of Missing Data. Statistical Methods and Applications, 17, 351-372.
http://dx.doi.org/10.1007/s10260-007-0090-4
[15] Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics. New York: Wiley.
http://dx.doi.org/10.1002/9780470316696
[16] Snijders, T. A. B., & Bosker, R. (2011). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Longdon: Sage Publications Ltd.
[17] Stuart, E. A., Azur, M., Frangakis, C., & Leaf, P. (2009). Multiple Imputation with Large Data Sets: A Case Study of the Children’s Mental Health Initiative. American Journal of Epidemiology, 169, 1133-1139.
http://dx.doi.org/10.1093/aje/kwp026
[18] Su, Y.-S., Gelman, A., Hill, J., & Yajima, M. (2011). Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box. Journal of Statistical Software, 45, 1-31.
[19] van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45, 1-67.

  
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