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

9223637金几乎每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。有人可能找到了正确的调查，但它遗漏了一个关键问题。研究人员可能拥有美国各州的数据，但并不是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。研究人员研究了这个问题的一个重要方面：在组织为横截面的时间序列的数据集的分析中处理丢失的数据；这是一个目前没有适当解决方案的问题。这一问题是调查人员进行的实质性研究的两个不同领域的一个重要障碍，一个涉及一系列总统“审判热”民调的数据，另一个涉及大量合法的选举数据。在寻找解决办法的过程中，调查人员发现，这个问题要普遍得多，至少隐含地出现在统计学、政治方法论以及政治学和其他领域的许多实质性研究领域。研究人员计划推广现有的“多重推算”统计方法，对数据进行数学模拟，以处理这样的数据。***

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. ***