The Promise and Limitations of Synthetic Data as a Strategy to Expand Access to State-Level Multi-Agency Longitudinal Data
The Promise and Limitations of Synthetic Data as a Strategy to Expand Access to State-Level Multi-Agency Longitudinal Data
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DOI：
10.1080/19345747.2019.1631421
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发表时间：
2019-07-22
影响因子：
1.8
通讯作者：
Zheng, Yating
中科院分区：
文献类型：
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作者：
Bonnery, Daniel;Feng, Yi;Zheng, Yating
政策制定者有使用州立教育纵向数据系统的需求，但监管数据披露的法律和政策限制了对此类数据的访问，安全担忧和风险仍然很高。完善的合成数据集在统计上模拟了派生变量的数据中变量之间的关系，但不包含代表实际人员的记录，为这些法律、政策、关切和风险提供了可行的解决方案。我们提供了一个开发合成数据系统的案例研究，并强调了合成数据的潜在应用。我们首先概述合成数据，它是什么，到目前为止它是如何被利用的，以及它在教育数据系统中应用的潜在好处和关注。然后，我们描述了我们由联邦政府资助的项目，提出了合成覆盖高中、中学后和劳动力数据的全州纵向数据系统所需的步骤。最后，为了作为其他机构考虑合成数据的模板，我们审查了我们在开发用于研究和政策评估目的的合成数据系统时所面临的挑战。
There is demand among policy-makers for the use of state education longitudinal data systems, yet laws and policies regulating data disclosure limit access to such data, and security concerns and risks remain high. Well-developed synthetic datasets that statistically mimic the relations among the variables in the data from which they were derived, but which contain no records that represent actual persons, present a viable solution to these laws, policies, concerns, and risks. We present a case study in the development of a synthetic data system and highlight potential applications of synthetic data. We begin with an overview of synthetic data, what it is, how it has been utilized thus far, and the potential benefits and concerns in its application to education data systems. We then describe our federally-funded project, proposing the steps required to synthesize a statewide longitudinal data system covering high school, postsecondary, and workforce data. Last, for use as a template for other agencies considering synthetic data, we review the challenges we have confronted in the development of our synthetic data system for research and policy evaluation purposes.