与暴力相关的研究中的相似模型:一个缺失数据的方法
Estela Capelas Barbosa1, Niels Blom2, Annie Bunce3
1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
PloS one
|January 14, 2025
概括
这项研究展示了一种新的方法,将调查和行政数据用于暴力研究. 创建一个合成数据集,通过克服数据访问障碍,增强对性暴力影响的理解.
科学领域:
- 社会科学 社会科学 社会科学
- 犯罪学 犯罪学
- 公共卫生 公共卫生
背景情况:
- 暴力研究由于数据访问限制和安全问题而分散.
- 现有的研究通常使用单个数据集分析暴力,限制了全面的理解.
- 将数据集结合起来,可以对暴力经历和健康后果进行纵向分析.
研究的目的:
- 通过结合调查和行政数据,提供创建合成数据集的概念证明.
- 探索跨多个部门的暴力研究中的模式和关联.
- 克服暴力研究中的数据链接障碍.
主要方法:
- 数据集成作为一个缺失的数据问题,使用多重归算与链式方程来处理数据集成.
- 来自英格兰和威尔士犯罪调查 (CSEW) 和英格兰和威尔士强奸危机 (RCEW) 行政数据的综合数据.
- 利用相似模型原理将CSEW中缺少的数据归纳到RCEW数据集中,创建一个合成的RCEW-CSEW数据集.
主要成果:
- 综合合成数据集中的效应大小反映了用于归算的源数据集中的效应大小.
- 综合数据集的变异性增加导致了较少的统计学上显著的估计.
- 该方法成功创建了一个合成组合数据集,用于分析与暴力有关的模式.
结论:
- 在暴力研究中,使用类似方法组合行政和调查数据集是可行的.
- 这种方法提供了一种创新且具有成本效益的方法来解决数据访问障碍.
- 综合数据集方法促进了对暴力经历和影响的多部门探索.
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