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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.

AbstractSurvey research methodology is evolving rapidly, as new technologies provide new opportunities. One of the areas of innovation regards the development of online interview best practices and the advancement of methods that allow researchers to measure the attention that respondents are devoting to the survey task. Reliable measurement of respondent attention can yield important information about the quality of the survey response. In this article, we take advantage of an innovative survey we conducted in 2018, in which we directly connect survey responses to administrative data, allowing us to assess the association between survey attention and response quality. We show that attentive survey respondents are more likely to provide accurate survey responses regarding a number of behaviors and attributes that we can validate with our administrative data. We discuss the best strategy to deal with inattentive respondents in surveys in light of our results.

The counting of votes in contemporary American elections is usually not completed on Election Night. There has been an increasing tendency for vote shares to shift toward Democratic candidates after Election Day in general elections, in particular, in recent U.S. elections. Leveraging important snapshots of precinct-level election returns and precinct-level demographic and political composition from Orange County, California, we conduct the first full-fledged analysis of the potential drivers of vote share shifts. Using an original large-scale post-election survey and unique snapshots of individual-level administrative records, we also provide the first analysis of the characteristics of voters whose ballots were tallied later versus earlier in the process. Far from being anomalous, our results indicate that the shifts are consistent with underlying precinct voter compositions and the order of precinct and mail ballot processing at the individual level in accordance with election administration practices. We find the same driving forces in North Carolina and Colorado, and discuss the consequences of the "Blue Shift" for public concerns about election integrity as states push policy changes regarding access to voting by mail.

ObjectiveAssuring voter confidence is important for the legitimacy of democratic elections. In this article, we test four hypotheses about the correlates of voter confidence at various levels of election administration.MethodsWe take advantage of a large online survey of registered voters in a single election jurisdiction, Orange County (CA), which was implemented immediately after the November 2018 midterm elections. We measure voting experience and social media usage using item response theory as well as voters' perceptions of various election or voter fraud, and estimate their association with voter confidence via logistic regression models.ResultsOur results show that voters who cast mail ballots are less confident about their own votes being counted correctly than in‐person voters. For both types of voters, those who have poor experiences with the voting process are much less likely to report confidence in the election. We also find that voters who have strong concerns about election fraud are less likely to report being confident in the election. Our last result indicates that information from news and social media is associated with a decline in voter confidence in election administration at the national level.ConclusionGiven the many conversations about election fraud that have occurred since the 2016 presidential election, this research provides important implications for election administration in future elections in the United States.

Does attentiveness matter in survey responses? Do more attentive survey participants give higher quality responses? Using data from a recent online survey that identified inattentive respondents using instructed-response items, we demonstrate that ignoring attentiveness provides a biased portrait of the distribution of critical political attitudes and behavior. We show that this bias occurs in the context of both typical closed-ended questions and in list experiments. Inattentive respondents are common and are more prevalent among the young and less educated. Those who do not pass the trap questions interact with the survey instrument in distinctive ways: they take less time to respond; are more likely to report nonattitudes; and display lower consistency in their reported choices. Inattentiveness does not occur completely at random and failing to properly account for it may lead to inaccurate estimates of the prevalence of key political attitudes and behaviors, of both sensitive and more prosaic nature.