估计政治科学中模型不确定性的程度和来源
Michael Ganslmeier1, Tim Vlandas2
1Centre for Computational Social Science, Department of Humanities, Arts and Social Sciences, University of Exeter, Exeter EX4 4PY, United Kingdom.
概括
量化政治学面临着显著的模型不确定性. 新的方法揭示了采样和测量选择,而不仅仅是共变量,驱动这种不确定性,影响影响影响方向.
科学领域:
- 政治科学 政治科学是指政治学.
- 量化方法 量化方法
- 社会科学研究 社会科学研究
背景情况:
- 在定量政治学中,评估模型不确定性至关重要.
- 现有的灵敏度分析往往忽视了对多个建模选择的同时考虑.
- 这限制了对研究结果的全面理解.
研究的目的:
- 开发一种系统的方法来评估跨多维的模型不确定性.
- 将极限边界分析与综合灵敏度分析的多层次方法结合起来.
- 调查政治科学研究中模型不确定性的来源和程度.
主要方法:
- 开发了一种新的灵敏度分析方法,整合了极限边界分析和多元宇宙概念.
- 系统评估跨维度的模型不确定性:控制集,固定效应,SE类型,样本选择和依赖变量运行.
- 将该方法应用于四个主要的政治学主题,产生超过36亿的估计.
主要成果:
- 证明了广泛的模型不确定性,影响了统计学意义和效应方向.
- 发现样本选择和依赖变量操作化比共变量空间更具影响力.
- 我们比较了三种方法 (1-邻居,物流,深度学习) 来估计规范选择的重要性.
结论:
- 政治科学中的模型不确定性主要是由采样和测量决策驱动的.
- 条件选择 (共变量) 的影响相对较小.
- 强调需要强有力的方法来评估和报告社会科学研究中的模型不确定性.
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