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Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation (2000)
| Venue: | American Political Science Review |
| Citations: | 419 - 50 self |
BibTeX
@ARTICLE{King00analyzingincomplete,
author = {Gary King and James Honaker and Anne Joseph and Kenneth Scheve},
title = {Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation},
journal = {American Political Science Review},
year = {2000},
volume = {95},
pages = {49--69}
}
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Abstract
We propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that "multiple imputation" is a superior approach to the problem of missing data scattered through one's explanatory and dependent variables than the methods currently used in applied data analysis. The reason for this discrepancy lies with the fact that the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise. In this paper, we adapt an existing algorithm, and use it to implement a generalpurpose, multiple imputation model for missing data. This algorithm is considerably faster and easier to use than the leading method recommended in the statistics literature. We also quantify the risks of current missing data practices, ...
Keyphrases
multiple imputation incomplete political science data alternative algorithm multiple imputation model way political scientist applied data analysis superior approach commercial statistical package statistic community computational algorithm data practice dependent variable considerable expertise statistic literature
