WHAT CAN WE LEARN WITH STATISTICAL TRUTH SERUM? DESIGN AND ANALYSIS OF THE LIST EXPERIMENT
WHAT CAN WE LEARN WITH STATISTICAL TRUTH SERUM? DESIGN AND ANALYSIS OF THE LIST EXPERIMENT
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DOI：
10.1093/poq/nfs070
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发表时间：
2013-01-01
影响因子：
3.4
通讯作者：
Glynn, Adam N.
中科院分区：
文献类型：
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作者：
Glynn, Adam N.
由于许多调查问题固有的敏感性，许多研究人员采用了一种间接提问技术，称为列表实验（或项目计数技术），以减少不诚实或逃避的回答。然而，列表实验的标准做法需要较大的样本量，仅利用均值差估计量，并且不提供每个受访者敏感项目的测量。本文件涉及所有这些问题。首先，本文提出了标准列表实验（和双列表实验）的设计原则，以减少偏差和方差，以及提供样本量的研究规划公式。其次，本文证明了一个响应级的概率测度的敏感项目可以推导出来。这为诊断、改进估计和回归分析提供了基础。本文中的技术说明了从2008-2009年美国国家选举研究（ANES）小组研究和适应这个实验的列表实验。
Due to the inherent sensitivity of many survey questions, a number of researchers have adopted an indirect questioning technique known as the list experiment (or the item-count technique) in order to reduce dishonest or evasive responses. However, standard practice with the list experiment requires a large sample size, utilizes only a difference-in-means estimator, and does not provide a measure of the sensitive item for each respondent. This paper addresses all of these issues. First, the paper presents design principles for the standard list experiment (and the double list experiment) for the reduction of bias and variance as well as providing sample-size formulas for the planning of studies. Second, this paper proves that a respondent-level probabilistic measure for the sensitive item can be derived. This provides a basis for diagnostics, improved estimation, and regression analysis. The techniques in this paper are illustrated with a list experiment from the 2008-2009 American National Election Studies (ANES) Panel Study and an adaptation of this experiment.