Statistical Analysis of List Experiments
Statistical Analysis of List Experiments
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DOI：
10.1093/pan/mpr048
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发表时间：
2012-12-01
影响因子：
5.4
通讯作者：
Imai, Kosuke
中科院分区：
文献类型：
--
作者：
Blair, Graeme;Imai, Kosuke
实证研究的有效性往往依赖于自我报告的行为和信念的准确性。然而，在调查中引出真实的答案是具有挑战性的，特别是在研究种族偏见、腐败和支持激进组织等敏感问题时。列表实验作为解决这一测量问题的一种可能的方法，近年来引起了人们的广泛关注。然而，许多研究人员，使用了一个简单的差异，在均值估计，这阻止了有效的检查受访者的特征和他们的敏感项目的反应之间的多元关系。此外，没有系统的手段来调查基本假设的作用。我们填补了这些空白，开发了一套新的统计方法列表实验。我们确定了常用的假设，提出了新的多元回归估计，并开发了方法来检测和调整潜在的违反关键假设。为了实证说明，我们分析了有关种族偏见的实验列表。开放源码软件可用于实施拟议的方法。
The validity of empirical research often relies upon the accuracy of self-reported behavior and beliefs. Yet eliciting truthful answers in surveys is challenging, especially when studying sensitive issues such as racial prejudice, corruption, and support for militant groups. List experiments have attracted much attention recently as a potential solution to this measurement problem. Many researchers, however, have used a simple difference-in-means estimator, which prevents the efficient examination of multivariate relationships between respondents' characteristics and their responses to sensitive items. Moreover, no systematic means exists to investigate the role of underlying assumptions. We fill these gaps by developing a set of new statistical methods for list experiments. We identify the commonly invoked assumptions, propose new multivariate regression estimators, and develop methods to detect and adjust for potential violations of key assumptions. For empirical illustration, we analyze list experiments concerning racial prejudice. Open-source software is made available to implement the proposed methodology.