Lukas F. Stoetzer

I am a Professor of Quantitative Methods at the Department of Philosophy, Politics and Economics at the University of Witten/Herdecke.

Before I worked at the Humboldt University of Berlin, at the excellence cluster SCRIPTS, at the Department of Political Science at University of Zurich, and a as Postdoctoral Fellow at the Massachusetts Institute of Technology, Department of Political Science, on a project that was funded by the Fritz-Thyssen Foundation. I graduated from University of Mannheim at the Graduate School of Economic & Social Sciences in May 2015.

My research in quantitative methods focuses on the application of causal inference and measurement methods. My subtantial area of research pertains to understanding political behaviour and its consequences in representative democracies. To find answers to the question how voters’ behaviour influences democratic quality, I develop quantitative research methods and build on formal theoretical decision making processes.

news

Jun 16, 2024	Publication in the American Journal of Political Science
Jun 13, 2024	Publication In Nature Climate Change
Oct 24, 2023	Two Publications in Electoral Studies

selected publications

How parties react to voter transitions

Tarik Abou-Chadi, and Lukas F Stoetzer

American Political Science Review 2020

Abs Bib Code Website

This letter investigates how voter transitions between parties affect parties’ policy positioning. While a growing literature investigates the role of election results as signals for parties’ policy adaption, it has mostly focused on vote changes of individual parties. However, parties do not know only whether they have won or lost in an election; they also have detailed information on which parties they won votes from and which parties they lost votes to. We make two arguments about how voter transitions should affect the strategic policy choices of political parties. First, when a party has lost votes to another party it will adapt its policy positions toward that party. Second, parties that have overall lost more votes become more likely to adapt their positions. Making use of a data set on individual voter transitions and party positions we can demonstrate that voter transitions indeed affect parties’ competitive behavior.
@article{abou2020parties, bibtex_show = {true}, status = {published}, preview = {apsr.jpg}, impact = {1}, website = {https://doi.org/10.1017/S0003055420000155}, title = {How parties react to voter transitions}, author = {Abou-Chadi, Tarik and Stoetzer, Lukas F}, journal = {American Political Science Review}, volume = {114}, number = {3}, pages = {940--945}, year = {2020}, publisher = {Cambridge University Press}, selected = {true}, code = {https://doi.org/10.7910/DVN/HULLNG} }
Eliciting beliefs as distributions in online surveys

Lucas Leemann, Lukas F Stoetzer, and Richard Traunmüller

Political Analysis 2021

Abs Bib Code Website

Citizens’ beliefs about uncertain events are fundamental variables in many areas of political science. While beliefs are often conceptualized in the form of distributions, obtaining reliable measures in terms of full probability densities is a difficult task. In this letter, we ask if there is an effective way of eliciting beliefs as distributions in the context of online surveys. Relying on experimental evidence, we evaluate the performance of five different elicitation methods designed to capture citizens’ uncertain expectations. Our results suggest that an elicitation method originally proposed by Manski (2009) performs well. It measures average citizens’ subjective belief distributions reliably and is easily implemented in the context of regular (online) surveys. We expect that a wider use of this method will lead to considerable improvements in the study of citizens’ expectations and beliefs.
@article{leemann2021eliciting, bibtex_show = {true}, status = {published}, preview = {pa.jpg}, website = {https://doi.org/10.1017/pan.2020.42}, title = {Eliciting beliefs as distributions in online surveys}, author = {Leemann, Lucas and Stoetzer, Lukas F and Traunm{\"u}ller, Richard}, impact = {1}, journal = {Political Analysis}, volume = {29}, number = {4}, pages = {541--553}, year = {2021}, publisher = {Cambridge University Press}, selected = {true}, code = {https://doi.org/10.7910/DVN/GEC2LS} }
Tracking and promoting the usage of a COVID-19 contact tracing app

Simon Munzert, Peter Selb, Anita Gohdes, Lukas F Stoetzer, and Will Lowe

Nature Human Behaviour 2021

Abs Bib Code Website

Digital contact tracing apps have been introduced globally as an instrument to contain the COVID-19 pandemic. Yet, privacy by design impedes both the evaluation of these tools and the deployment of evidence-based interventions to stimulate uptake. We combine an online panel survey with mobile tracking data to measure the actual usage of Germany’s official contact tracing app and reveal higher uptake rates among respondents with an increased risk of severe illness, but lower rates among those with a heightened risk of exposure to COVID-19. Using a randomized intervention, we show that informative and motivational video messages have very limited effect on uptake. However, findings from a second intervention suggest that even small monetary incentives can strongly increase uptake and help make digital contact tracing a more effective tool.
@article{munzert2021tracking, status = {published}, bibtex_show = {true}, preview = {nhb.png}, website = {https://www.nature.com/articles/s41562-020-01044-x}, title = {Tracking and promoting the usage of a COVID-19 contact tracing app}, impact = {1}, author = {Munzert, Simon and Selb, Peter and Gohdes, Anita and Stoetzer, Lukas F and Lowe, Will}, journal = {Nature Human Behaviour}, volume = {5}, number = {2}, pages = {247--255}, year = {2021}, publisher = {Nature Publishing Group}, selected = {true}, code = {https://github.com/simonmunzert/covid19-app-experiment} }
Causal Inference with Latent Outcomes

Lukas F Stoetzer, Xiang Zhou, and Marco Steenbergen

American Journal of Political Science 2024

Abs Website

While causal inference has become front and center in empirical political science, we know little about how to analyze causality with latent outcomes, such as political values, beliefs, and attitudes. In this article, we develop a framework for deﬁning, identifying, and estimating the causal effect of an observed treatment on a latent outcome, which we call the latent treatment effect (LTE). We describe a set of assumptions that allow us to identify the LTE and propose a hierarchical item response model to estimate it. We highlight an often overlooked exclusion restriction assumption, which states that treatment status should not affect the observed indicators other than through the latent outcome. A simulation study shows that the hierarchical approach offers unbiased estimates of the LTE under the identiﬁcation and modeling assumptions, whereas conventional two-step approaches are biased. We illustrate our proposed methodology using data from two published experimental studies