美国家庭的多维福利在一个精细的空间尺度使用合并的家庭调查
Kevin Ummel1, Miguel Poblete-Cazenave2,3, Karthik Akkiraju4
1University of California, Berkeley, Socio-Spatial Climate Collaborative, Berkeley, 94720, USA.
Scientific data
|January 29, 2024
概括
研究人员合并了美国政府调查数据,创建了一个集成的微数据集. 这个新资源使得家庭属性和幸福感的全面分析成为可能,克服了以前调查的局限性.
科学领域:
- 社会科学 社会科学 社会科学
- 数据科学数据科学数据科学
- 调查方法 调查方法
背景情况:
- 社会科学研究经常使用美国政府的调查.
- 现有的调查使用独立的样本和专业问题,限制了全面的分析.
- 这将研究问题限制在那些可以通过单个调查回答的问题上.
研究的目的:
- 通过合并来自多个美国政府调查的数据,创建一个集成的微数据集.
- 为了使家庭属性和福利维度的分析以前是不可能的.
- 为创建和验证融合微数据提供开源工具.
主要方法:
- 来自美国社区调查 (ACS) 的微数据与来自多项调查的供者变量合并.
- 包括2015年住宅能源消费调查 (RECS),2017年国家家庭旅行调查 (NHTS),2019年美国住房调查 (AHS) 和2015-2019年消费者支出调查 - 访谈 (CEI) 的数据.
- 开发开源R包,fusionModel,用于融合微数据分析.
主要成果:
- 一个集成的微数据集,将ACS微数据与RECS,NHTS,AHS和CEI的变量结合起来.
- 合并的数据集允许针对家庭属性和福祉提出新的研究问题.
- 融合ACS项目提供了用于融合微数据创建,分析和验证的通用工具.
结论:
- 美国政府多项调查的融合创造了一个强大的,集成的微数据集.
- 这种方法克服了单个调查分析的局限性,使得社会科学研究更加丰富.
- 开源的 fusionModel 软件包使对融合数据分析技术的访问更加民主化.
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