Anna-Carolina Haensch

Logo

Hi there!

View My GitHub Profile

Lecturer in Statistics & Data Science · University of Munich

Assistant Research Professor · University of Maryland

I work at the intersection of statistics, computational social science, and natural language processing.
My research focuses on missing data, synthetic data, and large-scale social science data, and I publish in both traditional statistics and methods journals as well as NLP conference proceedings.
I also care deeply about teaching and about making statistical and computational methods accessible – mostly using R and Python.

If you are interested in collaboration, teaching material, or data resources, feel free to reach out via my institutional profiles or LinkedIn.

Research & Publications

My research focuses on:

Selected Publications (Recent)

Full list of publications

Working Papers / Under Review

Clinton, J. D., Barari, S., Busby, E., Buskirk, T. D., Duch, R., Haensch, A., Hillygus, D. S., Kennedy, C., Munger, K., Rivers, D., Westwood, S. (APSA Task Force on AI and Polling). Public Opinion in the Age of AI.

Fuchs, A., Haensch, A., & Weber, W. AI for Survey Design: Generating and Evaluating Survey Questions with Large Language Models.

Reißinger, L., Li, Y., Haensch, A., & Sarna, N. Safer Prompts: Reducing IP Risk in Visual Generative AI.

Strasser-Ceballos, C., & Haensch, A. Determinants of Psychological Intimate Partner Violence Against Women with Children in Mexico - Insights from Model Based Boosting.

Peer-reviewed Journal Articles

von der Heyde, L., Haensch, A., & Weiß, B. (2025). Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation. Survey Research Methods 19(4), 355–370.

Herklotz, M., & Haensch, A. (2025). Exploring Computer Literacy Variance: Insights from an Introductory Statistical Programming Class. Journal of Statistics and Data Science Education, 1–18.

von der Heyde, L., Haensch, A., & Wenz, A. (2025). Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion. Social Science Computer Review.

Ewald, L. M., Bellettiere, J., Farag, T. H., Lee, K., Palani, S., Castro, E., Deen, A., Gillespie, C. W., Huntley, B. M., Tracy, A., Haensch, A., Kreuter, F., Weber, W., Zins, S., La Motte-Kerr, W., Li, Y., Stewart, K., Gakidou, E., & Mokdad, A. H. (2025). Insights on Pandemic Recovery: A Comprehensive Analysis from a 21-Country Online Survey. International Journal of Public Health.

Sommer, F., Schade, R., Prokosch, D., Bertolini Coelho, I, Haensch, A. (2025). Die (Un)wirksamkeit der Mietpreisbremse: die Ergebnisse der Mieten-Umfrage München. GuG - Grundstücksmarkt und Grundstückswert 2025 (2).

Haensch, A. & Schunck, R. (2024). Multiple Imputation for Systematically Missing Partner Variables in Survey Data. Sociological Methodology.

Drechsler, J. & Haensch, A. (2024). 30 Years of Synthetic Data. Statistical Science.

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. Sociological Methods & Research, Vol 53, Issue 4.

Haensch, Anna-Carolina, Bernd Weiß, Patricia Steins, Priscilla Chyrva, Katja Bitz. 2022. The semi-automatic categorization of open-ended questions on survey motivation and its reuse for attrition analysis. 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. 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. Statistical Journal of the IAOS, Vol. 37, No. 3: pp. 921–933.

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.

Conference Papers

Ma, B., Cao, Y., Sen, I., Haensch, A., Kreuter, F., Plank, B., & Hershcovich, D. (2026). Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics.

Ma, B., Yoztyurk, B., Haensch, A., Wang, X., Herklotz, M., Kreuter, F., Plank, B., Aßenmacher, M. (2025). Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1785–1809.

Fuchs, A., Noltenius, E., Weinzierl, C., Ma, B., & Haensch, A. (2025): Measuring Sexism in US Elections: A Comparative Analysis of X Discourse From 2020 to 2024. Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document Level Inferences (CODI 2025).

Kononykhina, O., Haensch, A., & Kreuter, F. (2025). Mind the Gap: Gender-based Differences in Occupational Embeddings. Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP).

Ma, B., Huang, C. & Haensch, A. (2025). Can Large Language Models Advance Crosswalks? The Case of Danish Occupation Codes. Proceedings of the 2025 NAACL Conference, 392–399.

Ippisch, N., Haensch, A., Simson, J., Beck, J., Herklotz, M. & Schierholz, M. (2025). Cracking the Code: Evaluating Zero-Shot Prompting Methods for Providing Programming Feedback. ICLR Workshop on Human-AI Coevolution.

Ma, B. & Wang, X. & Hu, T. & Haensch, A. & Hedderich, M. & Plank, B & Kreuter, F. (2024): The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models. Accepted at EMNLP Findings 2024.

Haensch, A., Ball, S., Herklotz, M., & Kreuter, F. (2024). Seeing ChatGPT Through Students’ Eyes: An Analysis of TikTok Data. IEEE BigSurv 2023 Conference Proceedings.

Books and Misc

Books

Kohler, U., Kreuter, F., & Haensch, A.-C. Data Analysis Using Stata. In preparation at Stata Press.

Haensch, A., Feder, B., Lane, J., Tombari, A., & Kreuter, F. (2024): Data Literacy and Evidence Building. Leanpub.

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

Misc

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. Harmonization Newsletter, Vol. 5, No. 1: 20–21.

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

R package

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

Survey Data Collection and Data Products

Haensch, Anna-Carolina, Kreuter, Frauke, La Motte-Kerr, W., Li, Yao, Stewart, Kathleen, Weber, Wiebke, Zins, Stefan, Castro, Emma, Deen, Amanda, Ewald, Louisa M., Gakidou, Emmanuela, Gillespie, Catherine W., Huntely, Bethany M., Tracy, Alison, Mokdad, Ali H., Bellettiere, John, Farag, Tamer H., Lee, Kristina, & Palani, Sid (2024). Pandemic Recovery Survey. GESIS, Köln. Datenfile Version 1.0.0.

Haensch, Anna-Carolina (2023). Overview of the “Syntucky” data for the participants of the Data literacy & Evidence building class by NYU/Accenture/UMD/KYStats/Coleridge Initiative.

CTIS UMD team at UMD and Facebook (2022). The University of Maryland Social Data Science Center Global COVID-19 Trends and Impact Survey in partnership with Facebook.

Talks

I regularly give talks and keynotes on survey methodology, missing data, synthetic data, and the role of large language models in social science research.

Selected Invited Talks

Full list of invited talks and conference presentations

Invited Talks

Indian Statistical Institute Platinum Jubilee Conference – 25.09.2025
“APPLICATIONS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN SAMPLE SURVEYS”

Computational Social Science: AI and Society – Exploring Inequality in the Digital Age. University of Mannheim, 15.05.2025.
“TikTok Data for Social Science Research: Access, Limitations, and Applications.”

BBAW Vorlesung der Technikwissenschaftliche Klasse 2024 – 28.11.2024
“Vertrauenswürdige KI: Warum ist das wichtig und worauf kommt es an?” (zusammen mit Tobias Schaeffter)

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 – 07.12.2023
“Can large language models predict how people vote? Evidence from Germany”

NFDI Series Show & Tell – 11.11.2022
Social Media-Daten in der Forschungspraxis II – Things to know when working with reddit data

Selected Conference Presentations

JSM 2025 – TeLLMe Why (AIn’t Nothing But a Survey)? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

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

I teach statistics and data science at the bachelor, master, and PhD level, with a focus on survey methodology, missing data, and applications of machine learning and LLMs in the social sciences.

Supervised Theses

Full list of supervised theses and courses

Supervised Theses

  • 5 PhD Theses on Statistical Education, application of ML methods in Social Sciences, and synthetic data generation with LLMs.
  • 15 × 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 × 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 × Bachelor Thesis (Sociology, University of Mannheim). 2021. All topics related to supervised classification of open-ended questions in surveys.

Seminars (Master / PhD Level)

  • “SURV748: Step by Step in Survey Weighting.” University of Maryland. Spring 2026.
  • “Fine Tuning LLMs for Data Augmentation and Synthesis” (with Tobias Holtdirk) AAPOR Pre-Conference. May 2025.
  • “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.
  • “Statistical Disclosure Control.” LMU Munich (together with Jörg Drechsler). Winter 2024.
  • “Church and Statistics.” LMU Munich (together with the Department of Catholic Theology). Winter 2024.
  • “Data Science Techniques for Survey Researchers.” GESIS Summer School. Summer 2024.
  • “FAIR workshop: Digital Trace Data in Social Science Research”. 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 2025.
  • “SURV699M: Review of Statistical Concepts.” IP-SDS. Summer 2024.
  • “SURV699M: Review of Statistical Concepts.” IP-SDS. Summer 2023.
  • “Statistics II for Social Scientists.” University of Munich. Summer 2021: 2 SWS (2×).
  • “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.

Lectures (Undergraduate Level)

  • “Statistics I for Social Scientists.” University of Munich. Winter 2026: 4 SWS.
  • “Statistics I for Social Scientists.” University of Munich. Winter 2025: 4 SWS.
  • “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) LMU Munich, SS 2017.
  • “Meta-Analysis in Social Research and Survey Methodology.” (together with Bernd Weiß and Jessica Wengrzik) GESIS Summer School 2018.

Awards, Grants & Service

I am involved in academic service and have received several awards and grants for research and teaching.

Board Memberships and Committees

Awards and Stipends

Stipends and Grants