Christoph Kern

Photo credit: Daniela Haupt

University of Mannheim
School of Social Sciences
A 5, 6
Building B – Room 222
68159 Mannheim

Phone: +49 621 181–2298
E-mail: c.kernuni-mannheim.de

Course name	Credits	Details	Term
Empirisches Forschungspraktikum II	10 ECTS	Course details	fall term

Research Interests
- Machine/ Statistical Learning
- Fair and Interpretable ML
- Topics in Survey Research (Digital Trace Data, Sensor Data, Non-probability Samples)
- Statistics (Structural Equation Modeling)
Career

Academic Career
Christoph Kern is a Post-Doctoral Researcher at the Professorship for Statistics and Methodology at the University of Mannheim and Research Assistant Professor at the Joint Program in Survey Methodology (JPSM) at the University of Maryland. He also is a Project Director at the Mannheim Centre for European Social Research (MZES) and member of the Mannheim Center for Data Science (MCDS). He received his PhD (Dr. rer. pol.) in social science from the University of Duisburg-Essen (UDE) in 2016. Before joining the University of Mannheim, he was a research associate at the Professorship for Empirical Social Science Research and Statistics at UDE.

CV
Publications
Bach, R. L., Kern, C., Amaya, A., Keusch, F., Kreuter, F., Hecht, J. & Heinemann, J. (2021). Predicting voting behavior using digital trace data. Social Science Computer Review : SSCOR, 39(5), 862–883. https://doi.org/10.1177/0894439319882896
Kern, C., Bach, R. L., Mautner, H. & Kreuter, F. (2021). Fairness in algorithmic profiling: A German case study. Ithaca, NY: Cornell University.
Kern, C., Weiß, B. & Kolb, J.-P. (2021). Predicting nonresponse in future waves of a probability-based mixed-mode panel with machine learning. Journal of Survey Statistics and Methodology : JSSAM, 1–24. https://doi.org/https://doi.org/10.1093/jssam/smab009
Kuppler, M., Kern, C., Bach, R. L. & Kreuter, F. (2021). Distributive justice and fairness metrics in automated decision-making: How much overlap is there? Ithaca, NY: Cornell University.
Pfisterer, F., Kern, C., Dandl, S., Sun, M., Kim, M. P. & Bischl, B. (2021). mcboost: Multi-Calibration Boosting for R. The Journal of Open Source Software : JOSS, 6(64), Article 3453, 1–3. https://doi.org/10.21105/joss.03453
Badillo-Goicoechea, E., Chang, T.-H., Kim, E., LaRocca, S., Morris, K., Deng, X., Chiu, S., Bradford, A., Garcia, A., Kern, C., Cobb, C., Kreuter, F. & Stuart, E. A. (2020). Global trends and predictors of face mask usage during the COVID-19 pandemic. Ithaca, NY: Cornell University.
Brian, K., Kern, C., Scott, M. J., Hunter, C. & Kumar, A. (2020). Workbooks. In I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter & J. Lane (eds.), Big data and social science : data science methods and tools for research and practice (2., S. 333–340). Milton ; Boca Raton, FL: CRC Press. https://doi.org/10.1201/9780429324383-13
Erlinghagen, M., Kern, C. & Stein, P. (2020). Migration, social stratification and dynamic effects on subjective well being. Advances in Life Course Research(Article 100393), 1–17. https://doi.org/10.1016/j.alcr.2020.100393
Gerdon, F., Theil, C. K., Kern, C., Bach, R. L., Kreuter, F., Stuckenschmidt, H. & Eckert, K. (2020). Exploring impacts of artificial intelligence on urban societies with social simulations. 40. Kongress der Deutschen Gesellschaft für Soziologie, Online.
Kern, C. (2020). Machine learning interpretation tools. In P. Atkinson (eds.), SAGE research methods: Foundations (S. 1–12). London: SAGE Publications. https://doi.org/10.4135/9781526421036913344
Kern, C., Höhne, J. K., Schlosser, S. & Revilla, M. (2020). Completion conditions and response behavior in smartphone surveys: A prediction approach using acceleration data. Social Science Computer Review : SSCORE, 1–19. https://doi.org/10.1177/0894439320971233
Kern, C., Li, Y. & Wang, L. (2020). Boosted kernel weighting – using statistical learning to improve inference from nonprobability samples. Journal of Survey Statistics and Methodology : JSSAM(smaa028), 1–26. https://doi.org/10.1093/jssam/smaa028
Kolb, J.-P., Weiß, B. & Kern, C. (2020). Using predictive modelling to identify panel nonresponse. In M. U. Mahidin (eds.), Proceeding ISI World Statistics Congress 2019 : Contributed paper session : 18 – 23 August 2019, Kuala Lumpur (S. 206–214). , Department of Statistics Malaysia (DOSM): Putrajaya.
Erlinghagen, M., Kern, C. & Stein, P. (2019). Internal migration, social stratiﬁcation and dynamic effects on subjective well being. SOEP papers on Multidisciplinary Panel Data Research, 1046. Berlin: DIW.
Kern, C., Klausch, T. & Kreuter, F. (2019). Tree-based machine learning methods for survey research. Survey Research Methods : SRM, 13(1), 73–93. https://doi.org/10.18148/srm/2019.v1i1.7395
Kern, C., Weiß, B. & Kolb, J.-P. (2019). A longitudinal framework for predicting nonresponse in panel surveys. Ithaca, NY: Cornell University.
Kern, C. & Stein, P. (2018). Modelling decision-making processes of regional mobility in a dyadic framework. European Sociological Review, 34(4), 433–451. https://doi.org/10.1093/esr/jcy012
Stein, P. & Kern, C. (2018). Konzeption eines dyadischen Modells zur Analyse von Entscheidungsprozessen räumlicher Arbeitsmarktmobilität im Mehrebenenkontext. In S. Quack (eds.), Transnationalisierung der Arbeit (S. 71–95). Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-658-20939-1
Kern, C. (2017). Dyadische Analyse regionaler Arbeitsmarktmobilität : Modellierung von Entscheidungsprozessen im Mehrebenenkontext . Wiesbaden: Springer VS. https://doi.org/http://doi.org/10.1007/978-3-658-17435-4
Kern, C. & Stein, P. (2016). Effect comparison in multilevel structural equation models with non-metric outcomes. In JSM Proceedings : papers presented at the Joint Statistical Meetings ... and other ASA-sponsored conferences (S. 3892–3901). , American Statistical Association: Alexandria, VA. https://doi.org/10.2139/ssrn.2879551
Kern, C. (2015). Modeling mobility dispositions from a multilevel perspective. Schmollers Jahrbuch, 135(1), 23–34. https://doi.org/10.3790/schm.135.1.23
Kern, C. & Stein, P. (2015). Comparing coefficients of nonlinear multivariate regression models between equations. Survey Research Methods : SRM, 9(3), 159–167. https://doi.org/10.18148/srm/2015.v9i3.6211
Stein, P. & Kern, C. (2015). Dyadische Modellierung regionaler Arbeitsmarktmobilität. In S. Lessenich (eds.), Routinen der Krise – Krise der Routinen : Verhandlungen des 37. Kongresses der Deutschen Gesellschaft für Soziologie in Trier 2014 (S. 520–531). , DFG: Trier.
Kern, C. (2014). Regional Structures and Mobility Dispositions: A Multilevel Proportional- & Partial-Proportional Odds Approach. SOEP papers on Multidisciplinary Panel Data Research, 681. Berlin: DIW. https://doi.org/10.2139/ssrn.2493094
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Projects
CAIUS: Consequences of AI for Urban Societies
FairADM – Fairness and discrimination in automated decision-making processes
Predicting Panel Drop-outs with Machine Learning
Supplementing and substituting survey data with big data

Christoph Kern

Research Interests

Career

Publications

Projects