Anna-Carolina Haensch
Synthetic Data | Multiple Imputation | Big Data in the Social Sciences | R Programming
LinkedIn | University of Munich | Twitter
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!
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.
Invited talks
Invited talks
CPSS @ KONVENS 2024 - 13.09.2024 “LLMs in Political and Social Science Research”
DZHW S3 Meeting - 12.06.2024 “Assessing bias in LLM-generated synthetic datasets: examining LLM personas in German and European elections.”
Long Night of the Universities Munich - 23.05.2024 “Of Data, Algorithms and Humans: Generative AI as Social Science Superhero?”
GESIS Lecture Series - 07.12.2023 „Can large language models predict how people vote? Evidence from Germany”
B-IT Lecture Series - 07.12.23 “Can large language models predict how people vote? Evidence from Germany”
NFDI Series Show & Tell - 11.11.22 – Social Media-Daten in der Forschungspraxis II - Things to know when working with reddit data
Selected conference presentations
JSM 2024 - Invited Panel Session: Future of Statistics and Data Science in the Era of ChatGPT and LLMs.
IC2S2 2024 - Oral presentation “Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion.”
AAPOR 2023 (presented by Leah von der Heyde) - Oral presentation “Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion.”
BigSurv 2023 - Oral presentation “Seeing ChatGPT Through Students’ Eyes: An Analysis of TikTok Data.”
WSC ISI 2023 - Oral presentation “Why do they leave? Why do they stay? Respondent’s motivation in a German mixed-mode Panel”
Pairfam 2022 (presented by Sebastian Sterl) - “The Whole is More than the Sum of its Parts – Studying Relationship Stability and Social Change by Pooling and Harmonizing Research Data from Various Infrastructures”
Workshop “Survey Climate and Trust in Scientific Surveys – Recent Developments and Controversial Issues” 2022 - “What do panelists say about their own participation motivation? Semi-automatic classification of an open-ended question on survey motivation”
ESRA 2021 (presented by Sonja Schulz) - “Measuring divorce risk with pooled survey data – A comparison between prospectively and retrospectively collected marriage biographies”
BigSurv 2020 - Oral presentation “Using supervised classification for categorizing answers to an open-ended question on panel participation motivation”
JSM 2018 - Oral presentation “Meta-Analysis of Survey-Based, Non-Experimental Individual Person Data with Heterogeneous Weighting Schemes”
Teaching
Supervised Theses
- (currently ongoing) 3 PhD Theses on Statistical Education, application of ML methods in Social Sciences, and synthetic data generation with LLMs.
- ~ 6 x Master Thesis (Statistics, University of Munich). Since 2022. Topics mostly related to the application of LLMs in synthetic data generation, missing data and application of ML methods in Social Sciences.
- ~ 15 x Bachelor Thesis (Statistics, University of Munich). Since 2022. Topics related to synthetic data (both traditional methods and LLMs), Multiple Imputation and the Covid-19 Trends and Impact Surveys.
- 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)
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“Fine Tuning LLMs for Data Augmentation and Synthesis” (with Tobias Holtdirk) New Directions: Bridging Natural Language Processing (NLP) and Survey Research at SurvAI-Day. October 2024
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“Statistical Disclosure Control.” LMU Munich (together with Jörg Drechsler). Winter 2024.
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“Church and Statistics.” LMU Munich (together with the Department of Catholic Theology). Winter 2024.
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“Data Science Techniques for Survey Researchers.” GESIS Summer School. Summer 2024.
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“FAIR workshop: Digital Trace Data in Social Science Resaerch”. TU Dortmund. Summer 2024.
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“SURV748: Step by Step in Survey Weighting.” Australian National University and Mannheim Business School. Spring 2024.
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“SURV748: Step by Step in Survey Weighting.” University of Maryland. Spring 2024.
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“Data Science Techniques for Survey Researchers.” GESIS Summer School. Summer 2023.
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“SURV748: Step by Step in Survey Weighting.” International Program in Survey and Data Science. Spring 2023.
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“Introduction to the world of Big Data & Analytics.” Mannheim Business School Summer School. Summer 2022.
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“SURV748: Step by Step in Survey Weighting.” International Program in Survey and Data Science. Spring 2021.
Seminars (Undergraduate level)
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“SURV699M: Review of Statistical Concepts.” International Program in Survey and Data Science. Summer 2024.
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“SURV699M: Review of Statistical Concepts.” International Program in Survey and Data Science. Summer 2023.
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“Statistics II for Social Scientists.” University of Munich. Summer 2021: 2 SWS (2x).
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“Quantitative Research Seminar II: Big Data in the Social Sciences. Data analysis.” University of Mannheim. Winter 2020: 4 SWS.
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“Quantitative Research Seminar I: Big Data in the Social Sciences. Data collection.” University of Mannheim. Spring 2020: 2 SWS.
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“Quantitative Research Seminar II: Big Data in the Social Sciences. Data analysis.” University of Mannheim. Winter 2019: 4 SWS.
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“Quantitative Research Seminar I: Big Data in the Social Sciences. Data collection.” University of Mannheim. Spring 2019: 2 SWS.
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“Quantitative Research Seminar II: Big Data in the Social Sciences. Data analysis.” University of Mannheim. Winter 2018: 4 SWS.
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“Quantitative Research Seminar I: Big Data in the Social Sciences. Data collection.” University of Mannheim. Spring 2018: 2 SWS.
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“Introduction to Data Collection.” University of Mannheim. Winter 2017: 2 SWS.
Lecture (Undergraduate level)
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“Statistics I for Social Scientists.” University of Munich. Winter 2025: 4 SWS.
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“Statistics II for Social Scientists.” University of Munich. Summer 2024: 4 SWS.
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“Statistics I for Social Scientists.” University of Munich. Winter 2024: 4 SWS.
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“Statistics II for Social Scientists.” University of Munich. Summer 2023: 4 SWS.
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“Statistics I for Social Scientists.” University of Munich. Winter 2023: 4 SWS.
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“Advanced Statistical Software Programming (R)” University of Munich. Summer 2022: 1 SWS.
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“Statistics II for Social Scientists.” University of Munich. Summer 2022: 4 SWS.
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“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.
Mix
Awards and Stipends
- 2022 AAPOR Burns “Bud” Roper Fellow Award
- 2022 (as part of the CTIS team) AAPOR Warren J. Mitofsky Innovators Award
- 2022 (as part of the CTIS team) AAPOR Policy Impact Award
- 2011-2017 Max-Weber-Programm (undergraduate and graduate stipend)
Stipends and Grants
- 2024 -2025 ~ 20000 Euro Promotion of teaching for the project RAINER - R Assistant IN Error Resolution
- 2023 ~ 10000 Euro LMU-NYU Scholarship
Board Membership
- 2024-2026 Eurostat EMOS Board
- 2023-2025 Ethikkommision Fakultät 16 LMU
- 2024-2026 Frauenbeauftragte Institut für Statistik, LMU
Current working papers and projects
Publications
Haensch, A.-C. & Schunck, R. (2024). Multiple imputation for systematically missing partner variables in survey data. Accepted at Sociological Methodology.
Drechsler, J. & Haensch, A.-C. (2024). 30 Years of Synthetic Data. Statistical Science.