Generalizing Multiple Imputation to Time Series Data, with Application to Survey Research and Evaluating Electoral Systems and Redistricting Plans

小行星9223637 几乎每一个在政治学中做过定量分析的人都遇到过这样的问题：在没有数据的地方需要数据。 人们可能找到了正确的调查，但它遗漏了一个关键问题。 研究人员可能有美国各州的数据，但并非所有州的所有信息都是已知的。 同样，研究人员可能有城市的数据，但没有美国其他地区的数据。 调查人员研究了这个问题的一个重要方面： 在分析按时间序列的横截面组织的数据集时处理缺失数据;目前还没有适当的解决方案。 这一问题在调查人员进行的实质性研究的两个不同领域中成为一个重要障碍，一个领域涉及由一系列总统“试探性”民意调查组成的数据，另一个领域涉及大量具有法律效力的选举数据。 在寻找解决方案的过程中，调查人员发现，这个问题要普遍得多，至少在统计学、政治方法学以及政治学和其他领域的许多实质性研究领域中都隐含着这个问题。 研究人员计划推广现有的“多重插补”统计方法，用数学方法模拟数据，以处理此类数据。 ***

9223637 King Nearly everyone who has ever done quantitative analysis in political science has come across the problem of needing data where none exist. One might have found the right survey, but it is missing a critical question. A researcher may have data on U.S states, but not all the information is known for all states. Likewise, a researcher may have data on the cities but not the rest of the United States. The investigators study one important aspect of this problem: handling missing data in the analysis of a dataset organized as a time series of cross-sections; a problem for which no adequate solution presently exist. This problem arose as an important obstacle in two distinct areas of the substantive research undertaken by the investigators, one involving data comprising a series of presidential "trial heat" polls and, the other, a large quantity of legilslative electoral data. In searching for a solution, the investigators found that the problem is much more general and has arisen at least implicitly in statistics, political methodology, and numerous areas of substantive research in political science and elsewhere. The researchers plan to generalize the existing statistical method of "multiple imputation," simulating data mathematically, to deal with data such as these. ***