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

将多重插补推广到时间序列数据，应用于调查研究以及评估选举制度和重新划分计划

• 批准号: 9223637
• 负责人: Gary King
• 金额: $ 7万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-08-01 至 1996-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9223637&HistoricalAwards=false
• 关键词: Generalizing; Multiple; Imputation; Time; Series

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. ***

9223637 King 几乎所有做过政治学定量分析的人都遇到过需要数据但又不存在数据的问题。 人们可能找到了正确的调查，但缺少一个关键问题。 研究人员可能拥有美国各州的数据，但并非所有州的所有信息都是已知的。 同样，研究人员可能拥有城市的数据，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：在分析组织为横截面时间序列的数据集时处理缺失数据；目前尚无适当解决方案的问题。 这个问题成为调查人员进行实质性研究的两个不同领域的重要障碍，一个涉及一系列总统“热火”民意调查的数据，另一个涉及大量立法选举数据。 在寻找解决方案的过程中，调查人员发现这个问题更为普遍，并且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中。 研究人员计划推广现有的“多重插补”统计方法，以数学方式模拟数据，以处理此类数据。 ***