Replication Data for: Relaxing the No Liars Assumption in List Experiment Analyses

The analysis of list experiments depends on two assumptions, known as \"no design effect\" and \"no liars\". The no liars assumption is strong and may fail in many list experiments. I relax the no liars assumption in this paper, and develop a method to provide bounds for the prevalence of sensitive behaviors or attitudes under a weaker behavioral assumption about respondents' truthfulness toward the sensitive item. I apply the method to a list experiment on the anti-immigration attitudes of California residents and on a broad set of existing list experiment datasets. The prevalence of different items and the correlation structure among items on the list jointly determine the width of the bounds estimates. In particular, the bounds tend to be narrower when the list consists of items of the same category, such as multiple groups or organizations, different corporate activities, and various considerations for politician decision-making. My paper illustrates when the full power of the no liars assumption is most needed to pin down the prevalence of the sensitive behavior or attitude, and facilitates estimation of the prevalence robust to violations of the no liars assumption for many list experiment applications.对列表实验（list experiment）的分析依赖于两项假设，即无设计效应（no design effect）与无说谎者（no liars）假设。“无说谎者”假设较强，在多数列表实验中可能不成立。本文放宽了“无说谎者”假设，基于受访者对敏感题项的诚实性这一更弱的行为假设，提出了一种可给出敏感行为或态度发生率区间估计的方法。本文将该方法应用于一项针对加州居民反移民态度的列表实验，以及多组现存的列表实验数据集。不同题项的发生率与列表内题项间的相关结构，共同决定了区间估计的宽度。具体而言，当列表中的题项属于同一类别时，区间估计往往更窄——例如多组群体或组织、各类企业活动，以及政治家决策时考量的各类因素。本文阐明了在何种场景下，需要借助“无说谎者”假设的全部效力来准确确定敏感行为或态度的发生率；同时，针对诸多列表实验应用场景，本文提出的方法可实现对“无说谎者”假设违背情况稳健的发生率估计。