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Relaxing the No Liars Assumption in List Experiment Analyses

  • Yimeng Li (a1)


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 bound 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.


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Author’s note: I thank Ines Levin for collecting and sharing with me the data I use in Section 3.1. Previous versions of this research were presented as a poster at the 34th Annual Meeting of the Society for Political Methodology (Polmeth 2017), and in a paper session at the 2018 Annual Meeting of the Midwest Political Science Association (MPSA 2018). I thank R. Michael Alvarez, Jonathan N. Katz, Seo-young Silvia Kim, Ines Levin, Lucas Núñez, Alejandro Robinson-Cortés, Robert Sherman, Matthew Shum, and participants at my poster and paper presentations at Polmeth 2017 and MPSA 2018 for discussions and comments. All errors are my own. Replication data are available in Li (2018) and an R function to implement the proposed bounds is available at

Contributing Editor: Jeff Gill



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