Synthetic Data User Testing and Dissemination

整个社会，行为，经济和健康科学的研究人员使用数据来测试有关广泛的个人和社会行为，决策和结果的假设。 政府统计机构定期收集对这一目的极为宝贵的数据。 然而，这些数据并不直接提供给研究界，因为数据提供者（响应者）的身份是数据本身的一部分。 因此，统计机构和科学界一直在开发方法，使分析有效和高度详细的数据提供给研究人员，同时保护个人隐私。一个特别有价值和敏感的数据是链接的行政数据，如纵向雇主-家庭数据（LEHD），纵向商业数据库（LBD）和调查与链接的行政数据（SIPP）。 这些数据集是在统计机构和NSF的支持下构建的。 这些数据非常详细，特别敏感，微观数据的获取仍然受到限制。 平衡保密保护与获取之间的紧张关系的一个办法是生成合成数据。 用于生成这种数据的过程开始于在给定机密微观数据的情况下估计待发布数据的后验预测分布（PPD）。 下一步是从PPD中抽取样本以生成发布的微观数据。 迄今为止，对基于适用于合成数据和实际数据的各种模型的推断质量进行了不准确的评估，因为只有有限数量的用户能够访问这两种数据来源。 这种评估需要纳入质量反馈循环，以改进综合数据，增加研究界对数据的使用。 该奖项为两个数据库的综合版本提供了这样一个反馈回路：人口普查局的收入和项目参与调查和纵向商业数据库。 其目标是扩大对数据的访问，增强反馈回路，并提供对这些合成数据的灵活和安全的访问。来自一系列学科的各种社会科学家将能够使用这种数据访问方法，并将提供详细的输入，这将指导未来数据质量的改进。

Researchers throughout the social, behavioral, economic, and health sciences use data to test hypotheses about a wide range of individual and social behaviors, decisions, and outcomes. Government statistical agencies regularly collect data that are extremely valuable for this purpose. However, these data are not made directly available to the research community because the data providers' (responents') identity is part of the data itself. Therefore statistical agencies and the scientific community have been developing methods to make analytically valid and highly detailed data available to researchers while simultaneously protecting individual privacy.A particularly valuable and sensitive kind of data is linked administrative data such as the Longitudinal Employer-Household Data (LEHD), the Longitudinal Business Database (LBD) and surveys with linked administrative data (SIPP). These datasets have been constructed with support from statistical agencies and the NSF. The highly detailed nature of these data make them particularly sensitive, and access to the micro-data remains restriced. One approach for balancing the tension between confientiality protection and access is the generation of synthetic data. The process for generating such data begins by estimating a posterior predictive distribution (PPD) of the to-be-released data given the confidential micro-data. The next step is to draw samples from the PPD to produce the released micro-data. The quality of inferences based on a wide variety of models applied to synthetic and actual data has been indaquately assessed to date because only a limited number of users have had access to both data sources. This kind of assessment needs to be integrated within a quality-feedback loop in order to improve synthetic data and increase the use of the data by the research community. This award facilitiates such a feedback loop for synthetic versions of two datasetss: the Census Bureau's Survey of Income and Program Participation and the Longitudinal Business Database. The goal is to broaden access to the data, enhance the feedback loop, and provide flexible and secure access to these synthetic data early releases.A variety of social scientists from a range of disciplines will be able to use this data access method and will provide detailed input that will guide future improvements in data quality.