Using Multiple Imputation for Vote Choice Data
A Comparison across Multiple Imputation Tools
Bibliographic Data
| ID | 13045705 |
|---|---|
| Authors | Frank C S Liu (0000-0003-4146-7291, National Sun Yat-sen University, corresponding author) |
| Year | 2014 |
| Volume | 04 |
| Issue | 02 |
| Pages | 39-46 |
| Publication date | 2014-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Open Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 2164-0505 • E-ISSN: 2164-0513 |
| Publisher | Bentham Science Publishers (PUBLISHER • AE) |
| DOI | 10.4236/ojps.2014.42006 |
| OpenAlex | W2063622673 |
| Language | EN |
| References cited | 15 |
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
Data mining · Econometrics · Imputation (statistics · Missing data · Political science · Politics · Presidential system · Statistics · Computer Science · Electoral Systems and Political Participation · Law · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
| Highly cited | No |
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