RIDIR: The Sub-National Data Archive System for Social and Behavioral Data

RIDIR：社会和行为数据的地方数据档案系统

• 批准号: 1925693
• 负责人: Kenneth Kollman
• 金额: $ 140.62万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925693&HistoricalAwards=false
• 关键词: RIDIR; Sub; National; Data; Archive

This project will create the Sub-National Data Archive System for Social and Behavioral Data. Research on political, social, and economic behavior increasingly depends on local, sub-national data, and on analysts' ability to combine data from multiple sources. Data may differ in geographic and time units and in measurement approaches and may have a variety of biases. Different data choices by researchers can lead to different conclusions and thus, across studies, can reduce the reliability, transparency, and replicability of empirical research. The Sub-National Data Archive System for Social and Behavioral Data will offer social scientists, policymakers, and the general public a suite of tools (1) to integrate different kinds of geographic data, (2) to account for differences in measurement across primary sources, and (3) to help assess the generalizability and robustness of research results across data sources. By making sub-national data more accessible, the project will help researchers inside and outside of academia test hypotheses, generate forecasts, evaluate the effectiveness of past and existing programs, and inform the allocation of scarce resources across competing initiatives. The overall goal is to improve understanding of how societies develop, prosper, and change, by enabling researchers to have easier access and effective use of highly-granular data from local areas. The Sub-National Data Archive System for Social and Behavioral Data addresses three persistent problems in social science research: mismatch between theoretical units of analysis and the scale of available data; the availability of multiple data sources for the same variables, which may be conflicting, overlapping or complementary; and a lack of generalizability of empirical results across alternative measurement strategies, or when using data from different sources and cases. This project will relieve these bottlenecks by producing a platform to develop highly-customizable integrated data with low technical barriers, enabling users to standardize sub-national units of analysis across countries, employ built-in methods where multiple data sources exist, and readily evaluate the robustness and generalizability of their results to alternative data and measurement choices (including shifting regional contexts). Core features of the platform will include: (1) a user-friendly web interface, where researchers can easily construct their own sub-national datasets from a large collection of archived data, (2) an open-source software package, which enables users to apply these tools to their own data, and produce a more customizable output based on individual specifications and needs, and (3) for users who wish to contribute original data to the platform, an archiving tool and data review process to ensure technical compliance and quality control. The program will build on previous data integration projects by providing a platform that covers almost every stage in the data life cycle.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将建立国家以下各级社会和行为数据档案系统。 对政治、社会和经济行为的研究越来越依赖于地方和次国家的数据，以及分析人员将多个来源的数据联合收割机结合起来的能力。 数据在地理和时间单位以及计量方法上可能有所不同，并可能存在各种偏差。 研究人员选择不同的数据可能会导致不同的结论，因此，在不同的研究中，可能会降低实证研究的可靠性，透明度和可复制性。 社会和行为数据的次国家数据档案系统将为社会科学家，政策制定者和公众提供一套工具（1）整合不同类型的地理数据，（2）解释主要来源之间的测量差异，以及（3）帮助评估跨数据源研究结果的普遍性和鲁棒性。 通过使地方数据更容易获得，该项目将帮助学术界内外的研究人员测试假设，生成预测，评估过去和现有计划的有效性，并为竞争性计划之间的稀缺资源分配提供信息。总体目标是通过使研究人员能够更容易地访问和有效地使用来自当地的高粒度数据，来提高对社会如何发展、繁荣和变化的理解。国家以下各级社会和行为数据档案系统解决了社会科学研究中三个长期存在的问题：理论分析单位与现有数据规模不匹配;同一变量有多种数据来源，这些数据可能相互冲突、重叠或互补;以及缺乏跨替代测量策略的经验结果的普遍性，或者当使用来自不同来源和案例的数据时。该项目将通过建立一个平台来缓解这些瓶颈，以开发高度可定制且技术障碍低的综合数据，使用户能够将各国的国家以下一级分析单位标准化，在存在多种数据来源的情况下采用内置方法，并随时评估其结果对替代数据和计量选择（包括不断变化的区域背景）的稳健性和普遍性。该平台的核心功能将包括：（1）用户友好的网络界面，研究人员可以很容易地从大量存档数据中构建自己的国家以下各级数据集，（2）开放源码软件包，使用户能够将这些工具应用于自己的数据，并根据个人规格和需要生成更可定制的输出，以及（3）为希望向平台提供原始数据的用户提供存档工具和数据审查流程，以确保技术合规性和质量控制。 该计划将建立在以前的数据集成项目的基础上，提供一个几乎涵盖数据生命周期每个阶段的平台。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Integrating Data Across Misaligned Spatial Units

• DOI: 10.1017/pan.2023.5
• 发表时间: 2023-03-23
• 期刊: POLITICAL ANALYSIS
• 影响因子: 5.4
• 作者: Zhukov, Yuri M.;Byers, Jason S.;Kollman, Ken
• 通讯作者: Kollman, Ken

Local Partisan Biases in Allocations of Foreign Aid: A Study of Agricultural Assistance in India

外国援助分配中的地方党派偏见：印度农业援助研究

• DOI: 10.1353/wp.2023.0000
• 发表时间: 2023
• 期刊: World Politics
• 影响因子: 5
• 作者: Min, Brian;Arima, Eugenio;Backer, David;Hicken, Allen;Kollman*, Ken;Selway, Joel
• 通讯作者: Selway, Joel

The Persistence of Ethnopopulist Support: The Case of Rodrigo Duterte's Philippines

民族民粹主义支持的持续存在：以罗德里戈·杜特尔特领导下的菲律宾为例

• DOI: 10.1017/jea.2022.29
• 发表时间: 2022
• 期刊: Journal of East Asian Studies
• 影响因子: 1.3
• 作者: Dulay, Dean;Hicken, Allen;Holmes, Ronald
• 通讯作者: Holmes, Ronald