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Welcome! I am passionate about research on missing data, synthetic data and big data in the social sciences. I also enjoy teaching (a lot) and programming (mostly in R and Python). If you have any questions, please feel free to contact me!

Publications

Teaching

Curriculum vitae

Education

PhD, Sociology 10/2017-06/2021
University of Mannheim, Germany

M. Sc., Survey Statistics 09/2014 – 09/2017
University of Bamberg, Germany

B. A., Political Science/Sociology 09/2011 – 09/2014
Ludwig-Maximilians-Universität (LMU) München, Germany

Professional Experience

Lecturer (Akademische Rätin a.Z.) Since 01/2024
University of Munich
Chair of Statistics and Data Science in Social Sciences and the Humanities

Visiting scholar 04/2023-08/2023
New York University
Wagner School of Public Policy

Postdoctoral researcher 04/2021-12/2023
University of Munich
Chair of Statistics and Data Science in Social Sciences and the Humanities

Assistant professor Since 08/2021
University of Maryland
International Program in Survey and Data Science

Postdoctoral researcher 10/2021 -12/2021
Institute for Employment Research
High-frequency Online Personal Panel (HOPP). Life and work situations in times of Corona

Doctoral researcher 08/2019-02/2021
University of Mannheim
International Program in Survey and Data Science

Doctoral researcher 09/2017-04/2021
GESIS – Leibniz Institute for the Social Sciences

Student assistant 2012 – 2017
Institute for Employment Research: Statistical Methods Centre
Ludwig-Maximilians-Universität (LMU): Chair of Empirical Political Research and Policy Analysis
Leibniz-Institute for Educational Trajectories (LIfBi e.V.)
Statistical Office for the Regions Berlin and Brandenburg: Mikrozensus.

Current working papers

von der Heyde, L., Haensch, A., & Wenz, A. (2023). Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion.

von der Heyde, L., Haensch, A., & Wenz, A. (2023). Assessing Bias in LLM-Generated Synthetic Datasets: The Case of German Voter Behavior.

Haensch, A.-C., Ball, S., Herklotz, M., & Kreuter, F. (2023). Seeing ChatGPT Through Students’ Eyes: An Analysis of TikTok Data. Accepted at IEEE BigSurv 2023 Conference Proceedings.

Drechsler, J. & Haensch, A.-C. (2023). 30 Years of Synthetic Data. Accepted at Statistical Science.

Systematically missing partner variables and possible multiple imputation strategies (with Reinhard Schunck)

Publications

Weiß, Bernd, Sonja Schulz, Lisa Schmid, Sebastian Sterl, Anna-Carolina Haensch. Harmonizing and Synthesizing Partnership Histories from Different German Survey Infrastructures. Chapter 14. In: (Eds. Irina Tomescu-Dubrow, Christof Wolf, Kazimierz M. Slomczynski, J. Craig Jenkins).

Anna-Carolina Haensch, Jonathan Bartlett, Bernd Weiß. Multiple imputation of partially observed covariates in discrete-time survival analysis. In: Sociological Methods & Research.

Haensch, Anna-Carolina, Bernd Weiß, Patricia Steins, Priscilla Chyrva, Katja Bitz. Forthcoming 2022. The semi-automatic categorization of open-ended questions on survey motivation and its reuse for attrition analysis. In: Frontiers in Sociology (Big Data and Machine Learning in Sociology).

Haensch, Anna-Carolina, Jacob Beck, Marie-Lou Sohnius. 2022. UMD survey offers real-time data-driven glimpse into Ukrainian well-being. University of Maryland Social Data Science Center blog.

Haensch, Anna-Carolina, Jacob Beck, Frauke Kreuter. Forthcoming June 2022. Die COVID-19 Trends and Impact Surveys. In: Neue Dimensionen in Data Science. Interdisziplinäre Ansätze und Anwendungen aus Wissenschaft und Wirtschaft (Eds. Barbara Wawrzyniak; Michael Herter).

Haensch, Anna-Carolina; Herklotz, Markus; Keusch, Florian; Kreuter, Frauke. 2021. The international program in survey and data science (IPSDS): a modern study program for working professionals. In: Statistical Journal of the IAOS, Vol. 37, No. 3: pp. 921-933.

Haensch, Anna-Carolina. 2021. Dealing with various flavors of missing data in ex-post survey harmonization and beyond. PhD Dissertation. University of Mannheim.

Haensch, Anna-Carolina, Drechsler, Jörg and Sarah Bernhard. 2020. TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding. (IAB-Discussion Paper, 29/2020), Nürnberg.

Haensch, Anna-Carolina, Corinna Stöckinger, and Doris Stingl. 2020. “Schluss mit Sterne gucken. Frequentistische Alternativen zum p-Wert.” In: Bad Science: Die dunkle Seite der Statistik (Eds. Rebekka Kluge, Florian Meinfelder).

Haensch, Anna-Carolina, Sonja Schulz, Sebastian Sterl, and Bernd Weiß. 2019. The HaSpaD (Harmonizing and Synthesizing Partnership Histories from Different Research Data Infrastructures) Project. In: Harmonization Newsletter, Vol. 5, No. 1: 20-21.

Kreitzscheck, Mathis, and Anna-Carolina Haensch. 2019. “Klopfet an, so wird euch aufgetan?: Teilnahmeverweigerung und Nonresponse Bias in der fünften Kirchenmitgliedschaftsuntersuchung.” Praktische Theologie 54 (1): 43-51.

Haensch, Anna-Carolina. 2016. “Armutsgefährdung in Berlin und Brandenburg 2014: Eine Analyse nach Lebensformen und Risikolagen.” Zeitschrift für amtliche Statistik Berlin Brandenburg 10 (1): 36-41.

Haensch, Anna-Carolina. 2014. Die Effekte von Koalitionspräferenzen und -erwartungen auf Wahlentscheidungen in Verhätniswahlsystemen. Münchener Beiträge zur Politikwissenschaft.

R package

CTIS. R based Global COVID-19 Trends and Impact Survey Microdata and Opendata API Interface (with Yue Xiong).

Teaching

Supervised Theses

(currently ongoing) 3 PhD Theses on Statistical Education, application of ML methods in Social Sciences, and synthetic data generation with LLMs.
~ 5 x Master Thesis (Statistics, University of Munich). Since 2022. Topics mostly related to the application of LLMs in synthetic data generation, application of ML methods in Social Sciences.
~ 10 x Bachelor Thesis (Statistics, University of Munich). Since 2022. Topics related to the Covid-19 Trends and Impact Surveys, synthetic data (both traditional methods and LLMs).
3 x Bachelor Thesis (Sociology, University of Mannheim). 2021. All topics related to supervised classification of open-ended questions in surveys.

Seminars (Master/PhD Level)

“Data Science Techniques for Survey Researchers.” GESIS Summer School. Summer 2024.
“FAIR workshop: Digital Trace Data in Social Science Resaerch”. TU Dortmund. Summer 2024.
“SURV748: Step by Step in Survey Weighting.” Australian National University and Mannheim Business School. Spring 2024.
“SURV748:Step by Step in Survey Weighting.” University of Maryland. Spring 2024.
“Data Science Techniques for Survey Researchers.” GESIS Summer School. Summer 2023.
“SURV748: Step by Step in Survey Weighting.” International Program in Survey and Data Science. Spring 2023.
“Introduction to the world of Big Data & Analytics.” Mannheim Business School Summer School. Summer 2022.
“SURV748: Step by Step in Survey Weighting.” International Program in Survey and Data Science. Spring 2021.

Seminars (Undergraduate level)

“SURV699M: Review of Statistical Concepts.” International Program in Survey and Data Science. Summer 2023.
“Statistics II for Social Scientists.” University of Munich. Summer 2021: 2 SWS (2x).
“Quantitative Research Seminar II: Big Data in the Social Sciences. Data analysis.” University of Mannheim. Winter 2020: 4 SWS.
“Quantitative Research Seminar I: Big Data in the Social Sciences. Data collection.” University of Mannheim. Spring 2020: 2 SWS.
“Quantitative Research Seminar II: Big Data in the Social Sciences. Data analysis.” University of Mannheim. Winter 2019: 4 SWS.
“Quantitative Research Seminar I: Big Data in the Social Sciences. Data collection.” University of Mannheim. Spring 2019: 2 SWS.
“Quantitative Research Seminar II: Big Data in the Social Sciences. Data analysis.” University of Mannheim. Winter 2018: 4 SWS.
“Quantitative Research Seminar I: Big Data in the Social Sciences. Data collection.” University of Mannheim. Spring 2018: 2 SWS.
“Introduction to Data Collection.” University of Mannheim. Winter 2017: 2 SWS.

Lecture (Undergraduate level)

“Statistics II for Social Scientists.” University of Munich. Summer 2024: 4 SWS.
“Statistics I for Social Scientists.” University of Munich. Winter 2024: 4 SWS.
“Statistics II for Social Scientists.” University of Munich. Summer 2023: 4 SWS.
“Statistics I for Social Scientists.” University of Munich. Winter 2023: 4 SWS.
“Advanced Statistical Software Programming (R)” University of Munich. Summer 2022: 1 SWS.
“Statistics II for Social Scientists.” University of Munich. Summer 2022: 4 SWS.
“Statistics I for Social Scientists.” University of Munich. Winter 2021: 4 SWS.

Teaching Assistant

“SURV699: Introduction to Official Statistics.” (taught by Walter Radermacher) International Program in Survey and Data Science.
“SURV726: Multiple Imputation.” (taught by Jörg Drechsler) International Program in Survey and Data Science.
“SURV725: Item Nonresponse and Imputation.” (taught by Jörg Drechsler) International Program in Survey and Data Science.
“Introduction to Empirical Research Methods in Political Science.” (taught by Paul Thurner) Ludwig-Maximilians-Universität (LMU) München SS 2017.

Other

“Meta-Analysis in Social Research and Survey Methodology.” (together with Bernd Weiß and Jessica Wengrzik) GESIS Summer School 2018.