Selection Bias in Comparative Research: The Case of Incomplete Data Sets
Selection Bias in Comparative Research: The Case of Incomplete Data Sets
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比较研究中的选择偏差：不完整数据集的情况
DOI：
10.1093/pan/mpg014
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发表时间：
2003
影响因子：
5.4
通讯作者：
S. Hug
中科院分区：
文献类型：
--
作者：
S. Hug
选择偏差是比较研究中一个重要而又常被忽视的问题。虽然比较案例研究对这个问题给予了一定的关注，但在更广泛的跨国研究中，这种情况较少，在这些研究中，这个问题可能通过所使用的数据产生的方式出现。本文讨论了三个例子：对新成立政党成功的研究，对抗议事件的研究，以及最近对种族冲突的研究。在所有情况下，手头的数据都可能受到选择偏差的影响。如果不考虑这个问题，就会导致对简单关系的估计出现严重偏差。经验例子说明了这些情况下问题的可能解决方案（Tobit模型的变体）。本文还讨论了蒙特卡罗模拟的结果，说明了所提出的估计过程在什么条件下会导致改进的结果。
Selection bias is an important but often neglected problem in comparative research. While comparative case studies pay some attention to this problem, this is less the case in broader cross-national studies, where this problem may appear through the way the data used are generated. The article discusses three examples: studies of the success of newly formed political parties, research on protest events, and recent work on ethnic conflict. In all cases the data at hand are likely to be afflicted by selection bias. Failing to take into consideration this problem leads to serious biases in the estimation of simple relationships. Empirical examples illustrate a possible solution (a variation of a Tobit model) to the problems in these cases. The article also discusses results of Monte Carlo simulations, illustrating under what conditions the proposed estimation procedures lead to improved results.