Long-Term Support for the National Elections Studies 1998-2001

1998-2001年全国选举研究的长期支持

• 批准号: 9707741
• 负责人: Virginia Sapiro
• 金额: $ 395.43万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-02-01 至 2003-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9707741&HistoricalAwards=false
• 关键词: Long; Term; Support; National; Elections

This award provides continued support of the Foundation's long-term commitment for the National Election Studies (NES). The National Science Foundation's Political Science Program supports the NES research program to sustain and enhance the diversified data base that undertakes basic research on voting, public opinion, and political participation in the United States. The current award provides support for the 1998 Congressional elections and the 2000 Presidential election. The award provides for continuation of the core time-series data collection which has been supported in part by the NSF since 1970 and completely since 1978. The Board of Overseers will continue to function as a very real partner in study planning and will maintain its full role as trustee and representative of the larger community of users of the NES data. There are opportunities for the diverse national social community to participate directly in the design and execution of NES studies. There are resources for improving the quality of the core NES time-series data and for testing innovations in theory, measurement, and data collection. This research constitutes the most important data base for the study of American politics and American political behavior.

该奖项为基金会对国家选举研究（内斯）的长期承诺提供了持续的支持。 国家科学基金会的政治科学计划支持内斯研究计划，以维持和加强多样化的数据库，承担对美国投票，民意和政治参与的基础研究。 目前的奖项为1998年国会选举和2000年总统选举提供支持。 该合同规定继续进行核心时间序列数据收集工作，自1970年以来，国家科学基金会部分支持了这一工作，自1978年以来，这一工作完全得到了国家科学基金会的支持。 监督委员会将继续作为研究规划中的一个非常真实的伙伴发挥作用，并将继续充分发挥其作为内斯数据广大用户的受托人和代表的作用。 不同的国家社会团体有机会直接参与内斯研究的设计和执行。 这些资源可用于提高核心内斯时间序列数据的质量，并用于测试理论、测量和数据收集方面的创新。 这项研究构成了研究美国政治和美国政治行为的最重要的数据基础。