About

Jan G. Voelkel is an Assistant Professor at the Jeb E. Brooks School of Public Policy at Cornell University. His research examines the conditions under which people support practices, leaders, and organizations that promise social change. His work has been published in leading journals such as Science, Proceedings of the National Academy of Sciences, and Nature Human Behaviour, and has been featured in major media outlets including The New York Times, the Washington Post, The Atlantic, Die Zeit, and Der Tagesspiegel. Jan has received several awards, including the New Investigator Award from the Behavioral Science & Policy Association, the 2025 Social Issues Dissertation Award from the Society for the Psychological Study of Social Issues, the Open Science Innovator Award from Stanford's Center for Open and Reproducible Science, and Stanford's Centennial Teaching Assistant Award. He earned a PhD and an MA in Sociology from Stanford University, an MS in Social and Behavioral Sciences from Tilburg University, and a BS in Social Sciences from the University of Cologne. Originally from the small village of Appeldorn in Germany, Jan enjoys puzzling over moral dilemmas, playing racket sports, and planning political murder mystery parties in his spare time.

Research topics

• Political Science
• Psychology
• Social psychology
• Law
• Statistics
• Humanities
• Computer Science
• Artificial Intelligence
• Mathematics
• Sociology
• Political economy
• Public relations
• Medicine
• Economics
• Econometrics
• Psychiatry
• Philosophy

Selected publications

• Megastudy testing 25 treatments to reduce antidemocratic attitudes and partisan animosity

  2023 · 43 citations

  1st authorCorresponding
  • Political Science
  • Sociology
  • Political Science

  Scholars warn that partisan divisions in the mass public threaten the health of American democracy. We conducted a megastudy (n = 32,059 participants) testing 25 treatments designed by academics and practitioners to reduce Americans’ partisan animosity and antidemocratic attitudes. We find that many treatments reduced partisan animosity, most strongly by highlighting relatable sympathetic individuals with different political beliefs or by emphasizing common identities shared by rival partisans. We also identify several treatments that reduced support for undemocratic practices – most strongly by correcting misperceptions of rival partisans’ views or highlighting the threat of democratic collapse – which shows that antidemocratic attitudes are not intractable. Taken together, the study’s findings identify promising general strategies for reducing partisan division and improving democratic attitudes, shedding theoretical light on challenges facing American democracy.

  PublisherOA PDFDOI

• Insights into the accuracy of social scientists’ forecasts of societal change

  Nature Human Behaviour · 2023 · 67 citations

  • Econometrics
  • Economics
  • Mathematics
  PublisherOA PDFDOI

• Interventions reducing affective polarization do not necessarily improve anti-democratic attitudes

  Nature Human Behaviour · 2022 · 183 citations

  1st authorCorresponding
  • Political Science
  • Social psychology
  • Psychology
  DOI

• The JASP guidelines for conducting and reporting a Bayesian analysis

  Psychonomic Bulletin & Review · 2020 · 1062 citations

  • Psychology
  • Computer Science
  • Artificial Intelligence

  Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

  DOI

• A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP

  L’Année psychologique · 2020 · 269 citations

  • Humanities
  • Humanities
  • Mathematics

  L’analyse de variance (ANOVA) est la procédure standard utilisée pour l’inférence statistique dans les plans factoriels. En règle générale, les analyses de variance sont exécutées à l’aide de statistiques fréquentistes, où les valeurs p déterminent la significativité statistique en termes de « tout ou rien ». Ces dernières années, l’approche bayésienne de la statistique inférentielle est de plus en plus considérée comme une alternative légitime à la valeur p . Toutefois, l’adoption généralisée des statistiques bayésiennes, et en particulier de l’ANOVA bayésienne, est limitée par le fait que les concepts bayésiens sont rarement enseignés dans les cours de statistiques appliquées. Par conséquent, les utilisateurs peuvent ne pas savoir comment effectuer une ANOVA bayésienne et en interpréter les résultats. Nous fournissons ici un guide pour réaliser et interpréter une ANOVA bayésienne avec JASP, un logiciel statistique open-source ayant une interface utilisateur graphique. Nous expliquons les concepts clés de l’ANOVA bayésienne à l’aide de deux exemples empiriques.

  DOI

Frequent coauthors

• Robb Willer

  Stanford University

  63 shared

• Mélanie Söderström

  Stanford University

  33 shared

• Krista Byers‐Heinlein

  Concordia University

  33 shared

• Heidi A. Baumgartner

  University of Manitoba

  33 shared

• Nicolás Alessandroni

  Concordia University

  33 shared

• Michael C. Frank

  Stanford University

  33 shared

• J. Kiley Hamlin

  University of British Columbia

  33 shared

• Francis Yuen

  Stanford University

  17 shared

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