Marcel Neunhoeffer
Year: 2016
Center: Social Sciences
Supervisor: Thomas Gschwend
Marcel Neunhoeffer holds a Bachelor’s Degree in Governance and Public Policy – with a Major in Political Science and a Minor in Economics – from the University of Passau. In 2011 he spent one semester abroad at Illinois State University, USA. In 2013 and 2014 he worked on two election campaigns. Then, he joined the M.A. Political Science at the University of Mannheim in Fall 2014. Alongside his studies in Mannheim he worked as a research assistant at the Chair of Quantitative Methods in the Social Sciences, worked for a Field Experiment at the MZES and for the state ministry in Baden-Württemberg. For his Master’s thesis he implemented a field experiment within a major campaign to test the effect of direct mail on voting behavior.
Research interests:
- Election campaigns
- Voting behavior
- Coalition formation
- Methods in the Political Sciences (esp. Experiments and Field Experiments)
Publications
2024
- Arnold, C., Biedebach, L., Küpfer, A. & Neunhoeffer, M. (2024). The role of hyperparameters in machine learning models and how to tune them. Political Science Research and Methods : PSRM, 12(4), 841–848. https://doi.org/10.1017/psrm.2023.61
2023
- Lehrer, R., Bahnsen, O., Müller, K., Neunhoeffer, M., Gschwend, T. & Juhl, S. (2023). Rallying around the leader in times of crises: The opposing effects of perceived threat and anxiety. European Journal of Political Research, 1–22. https://doi.org/10.1111/1475-6765.12717
- Rittmann, O., Neunhoeffer, M. & Gschwend, T. (2023). How to improve the substantive interpretation of regression results when the dependent variable is logged. Political Science Research and Methods : PSRM, 1–9. https://doi.org/10.1017/psrm.2023.29
2022
- Breznau, N., Rinke, E. M., Wuttke, A., Nguyen, H. H. V., Adem, M., Adriaans, J., Alvarez-Benjumea, A., Andersen, H. K., Auer, D., Azevedo, F., Bahnsen, O., Balzer, D., Bauer, G., Bauer, P. C., Baumann, M., Baute, S., Benoit, V., Bernauer, J., Berning, C., Berthold, A., Bethke, F. S., Biegert, T., Blinzler, K., Blumenberg, J. N., Bobzien, L., Bohman, A., Bol, T., Bostic, A., Brzozowska, Z., Burgdorf, K., Burger, K., Busch, K. B., Carlos-Castillo, J., Chan, N., Christmann, P., Connelly, R., Czymara, C., Damian, E., Ecker, A., Edelmann, A., Eger, M. A., Ellerbrock, S., Forke, A., Forster, A., Gaasendam, C., Gavras, K., Gayle, V., Gessler, T., Gnambs, T., Godefroidt, A., Grömping, M., Groß, M., Gruber, S., Gummer, T., Hadjar, A., Heisig, J. P., Hellmeier, S., Heyne, S., Hirsch, M., Hjerm, M., Hochman, O., Hövermann, A., Hunger, S., Hunkler, C., Huth, N., Ignácz, Z. S., Jacobs, L., Jacobsen, J., Jaeger, B., Jungkunz, S., Jungmann, N., Kauff, M., Kleinert, M., Klinger, J., Kolb, J.-P., Kołczyńska, M., Kuk, J., Kunißen, K., Kurti Sinatra, D., Langenkamp, A., Lersch, P. M., Löbel, L.-M., Lutscher, P., Mader, M., Madia, J. E., Malancu, N., Maldonado, L., Marahrens, H., Martin, N., Martinez, P., Mayerl, J., Mayorga, O. J., McManus, P., McWagner, K., Meeusen, C., Meierrieks, D., Mellon, J., Merhout, F., Merk, S., Meyer, D., Micheli, L., Mijs, J., Moya, C., Neunhoeffer, M., Nüst, D., Nygård, O., Ochsenfeld, F., Otte, G., Pechenkina, A. O., Prosser, C., Raes, L., Ralston, K., Ramos, M. R., Roets, A., Rogers, J., Ropers, G., Samuel, R., Sand, G., Schachter, A., Schaeffer, M., Schieferdecker, D., Schlueter, E., Schmidt, R., Schmidt, K. M., Schmidt-Catran, A., Schmiedeberg, C., Schneider, J., Schoonvelde, M., Schulte-Cloos, J., Schumann, S., Schunck, R., Schupp, J., Seuring, J., Silber, H., Sleegers, W. W. A., Sonntag, N., Staudt, A., Steiber, N., Steiner, N. D., Sternberg, S., Stiers, D., Stojmenovska, D., Storz, N., Striessnig, E., Stroppe, A.-K., Teltemann, J., Tibajev, A., Tung, B., Vagni, G., Van Assche, J., Van der Linden, M., Van der Noll, J., Van Hootegem, A., Vogtenhuber, S., Voicu, B., Wagemans, F., Wehl, N., Werner, H., Wiernik, B. M., Winter, F., Wolf, C., Yamada, Y., Zhang, N., Ziller, C., Zins, S. & Żółtak, T. (2022). Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences of the United States of America : PNAS, 119(44, Article e2203150119), 1–8. https://doi.org/10.1073/pnas.2203150119
- Gschwend, T., Müller, K., Munzert, S., Neunhoeffer, M. & Stoetzer, L. F. (2022). The Zweitstimme model: A dynamic forecast of the 2021 German federal election. PS : Political Science & Politics, 55(1), 85–90. https://doi.org/10.1017/S1049096521000913
2020
- Neunhoeffer, M., Gschwend, T., Munzert, S. & Stoetzer, L. F. (2020). Ein Ansatz zur Vorhersage der Erststimmenanteile bei Bundestagswahlen. Politische Vierteljahresschrift : PVS, 61(1), 111–130. https://doi.org/10.1007/s11615-019-00216-3
2021
- Neunhoeffer, M., Wu, S. & Dwork, C. (2021). Private Post-GAN Boosting. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3–7, 2021 : Poster Presentations (S. 1–17). , OpenReview.net: Austria.
2023
- Neunhoeffer, M. (2023). Generative adversarial nets for social scientists. [Doctoral dissertation, Universität Mannheim].
Credit: Farina Stock