Resources

This research finds that a majority of Trump voters in the survey sample falsely believed that election fraud was widespread, and that Trump won the election. It also finds that Trump conceding or losing his legal challenges would likely lead a majority of Trump voters to accept Biden’s victory as legitimate, although 40% said they would continue to view Biden as illegitimate regardless.

This book examines the dynamics behind shifts in voter registration rates across the states.

This survey report focuses on election misinformation, fraud narratives, or public misperceptions and their effects on confidence in U.S. elections. It is relevant because beliefs about fraud and exposure to misleading claims are central mechanisms through which confidence in election outcomes rises or falls. For this dataset, it helps explain why the 2020 election became a turning point in public debates over fraud, mail voting, certification, and legitimacy.

This academic article studies how messages from political elites influence public confidence in elections and acceptance of democratic norms.

Report offering solutions on how public information campaigns by state election officials could mitigate polarized trust in election integrity.

In this paper, authors use snapshots of voter registration files (VRF) over time and machine learning models to test the effectiveness of unsupervised anomaly detection methods in detecting VRF modifications. They find that statistical models comparing administrative districts within a short time span and non-negative matrix factorization are most effective for surfacing anomalous events for review.

This MIT Election Data + Science Lab analysis explains the 2020 “blue shift,” where later-counted ballots disproportionately favored Democrats, and why that pattern mattered for public interpretation of results.

In this paper, authors match a high-quality, random sample of the U.S. population to multiple lists revealing that at least 11% of the adult citizenry is not on a voter list. An additional 12% is mislisted (i.e., not living at their recorded address).

In this article, using data from Orange County, California, the researchers develop two methods for evaluating the quality of voter registration data as it changes over time: (a) generating audit data by repeated record linkage across periodic snapshots of a given database and monitoring it for sudden anomalous changes and (b) identifying duplicates via an efficient, automated duplicate detection, and tracking new duplicates and deduplication efforts over time.

This research summary addresses how mail voting, absentee-ballot procedures, or claims about mail-ballot fraud affect voter confidence and perceived legitimacy. It is relevant because beliefs about fraud and exposure to misleading claims are central mechanisms through which confidence in election outcomes rises or falls. For this dataset, it helps explain why the 2020 election became a turning point in public debates over fraud, mail voting, certification, and legitimacy.

This study uses an experiment to examine how voting method impacts voter confidence. Voters were randomly assigned to either an in-person or absentee voting condition. Participants assigned to the absentee condition expressed lower levels of confidence that their votes would be counted correctly than those assigned to the in-person voting condition

This study investigates the reliability of Florida’s voter registration files through a phone survey, asking respondents to verify their records. Authors find 17.7% of registrants fail to verify at least one identifying piece of information.