Akademische Rätin a.Z. & Group Leader · Social Data Science and AI Lab (SODA), LMU Munich

Assistant Research Professor · Joint Program in Survey Methodology, University of Maryland

I am a social science methodologist working at the intersection of survey methodology, statistics, and computational social science. My research asks how we can measure social phenomena validly when data are incomplete, confidential, or increasingly generated or processed by AI. I work on three connected strands:

• Missing and synthetic data – multiple imputation in complex survey and harmonized data, synthetic data for privacy and data access.
• Large language models in survey research – questionnaire design and evaluation, coding of open-ended responses, silicon sampling and the limits of LLM-simulated public opinion, LLM-based imputation.
• Survey methodology and data quality – nonresponse and participation motivation, weighting, ex-post harmonization of large-scale social science data.

I publish in sociology and methods journals (Sociological Methodology, Sociological Methods & Research, Public Opinion Quarterly, Statistical Science) as well as in NLP venues (ACL, EACL, EMNLP, NAACL). I lead a research group on AI and surveys at LMU and mentor doctoral researchers in social data science. I hold a PhD in Sociology from the University of Mannheim (2021).

Contact: C.Haensch@lmu.de · SODA profile · GitHub

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Research & Publications

Selected Publications

• Haensch, A.-C. & Haziza, D. (forthcoming). On the Use of Artificial Intelligence and Machine Learning for Official Statistics. The Canadian Journal of Statistics.
• von der Heyde, L., Haensch, A.-C., & Wenz, A. (2026). Vox Populi, Vox AI? Using Large Language Models to Estimate German Vote Choice. Social Science Computer Review, 44(3), 549–571.
• Haensch, A.-C. & Schunck, R. (2025). Multiple Imputation for Systematically Missing Partner Variables in Survey Data. Sociological Methodology, 55, 269–299.
• Drechsler, J. & Haensch, A.-C. (2024). 30 Years of Synthetic Data. Statistical Science, 39(2), 221–242.
• Ahnert, G., Haensch, A.-C., Plank, B., & Strohmaier, M. (2026). Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models. Proceedings of ACL 2026.

Full list of publications

Peer-reviewed Journal Articles

Haensch, A.-C., & Haziza, D. (forthcoming). On the Use of Artificial Intelligence and Machine Learning for Official Statistics. The Canadian Journal of Statistics / La revue canadienne de statistique.

Fuchs, A., Haensch, A.-C., & Weber, W. (accepted). AI for Survey Design: Generating and Evaluating Survey Questions with Large Language Models. Public Opinion Quarterly.

Strasser Ceballos, C., & Haensch, A.-C. (2026). Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure – A Machine Learning Approach. Journal of Computational Social Science, 9, 64.

von der Heyde, L., Haensch, A.-C., & Wenz, A. (2026). Vox Populi, Vox AI? Using Large Language Models to Estimate German Vote Choice. Social Science Computer Review, 44(3), 549–571.

von der Heyde, L., Haensch, A.-C., Weiß, B., & Daikeler, J. (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.-C. (2025). Exploring Computer Literacy Variance: Insights from an Introductory Statistical Programming Class. Journal of Statistics and Data Science Education, 1–18.

Haensch, A.-C., & Schunck, R. (2025). Multiple Imputation for Systematically Missing Partner Variables in Survey Data. Sociological Methodology, 55, 269–299.

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.-C., 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, 70, 1607601.

Drechsler, J., & Haensch, A.-C. (2024). 30 Years of Synthetic Data. Statistical Science, 39(2), 221–242.

Haensch, A.-C., Bartlett, J., & Weiß, B. (2024). Multiple Imputation of Partially Observed Covariates in Discrete-Time Survival Analysis. Sociological Methods & Research, 53(4), 2019–2045.

Haensch, A.-C., Weiß, B., Steins, P., Chyrva, P., & Bitz, K. (2022). The Semi-Automatic Categorization of Open-Ended Questions on Survey Motivation and Its Reuse for Attrition Analysis. Frontiers in Sociology.

Haensch, A.-C., Herklotz, M., Keusch, F., & Kreuter, F. (2021). The International Program in Survey and Data Science (IPSDS): A Modern Study Program for Working Professionals. Statistical Journal of the IAOS, 37(3), 921–933.

Kreitzscheck, M., & Haensch, A.-C. (2019). Klopfet an, so wird euch aufgetan? Teilnahmeverweigerung und Nonresponse Bias in der fünften Kirchenmitgliedschaftsuntersuchung. Praktische Theologie, 54(1), 43–51.

Haensch, A.-C. (2016). Armutsgefährdung in Berlin und Brandenburg 2014: Eine Analyse nach Lebensformen und Risikolagen. Zeitschrift für amtliche Statistik Berlin Brandenburg, 10(1), 36–41.

Peer-reviewed Conference Papers

Ma, B., Yao, Y., & Haensch, A.-C. (2026). Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry. Findings of ACL 2026, 16933–16957.

Ahnert, G., Haensch, A.-C., Plank, B., & Strohmaier, M. (2026). Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models. Proceedings of the 64th Annual Meeting of the ACL (Long Papers), 41554–41577.

Ippisch, N., Herklotz, M., Haensch, A.-C., & Schwemmer, C. (2026). Beyond Correctness: Evaluating and Improving LLM Feedback in Statistical Education. Proceedings of CSEDU 2026.

Ma, B., Cao, Y., Sen, I., Haensch, A.-C., Kreuter, F., Plank, B., & Hershcovich, D. (2026). Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation. Proceedings of EACL 2026.

Ma, B., Yoztyurk, B., Haensch, A.-C., 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 ACL 2025 (Long Papers), 1785–1809.

Fuchs, A., Noltenius, E., Weinzierl, C., Ma, B., & Haensch, A.-C. (2025). Measuring Sexism in US Elections: A Comparative Analysis of X Discourse From 2020 to 2024. CODI 2025.

Kononykhina, O., Haensch, A.-C., & Kreuter, F. (2025). Mind the Gap: Gender-based Differences in Occupational Embeddings. GeBNLP 2025.

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

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

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

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

Under Review and Preprints

Sen, I., Ma, B., Ahnert, G., Haensch, A.-C., Holtdirk, T., Kreuter, F., & Strohmaier, M. Connecting Natural Language Processing and Survey Methodology: Potentials, Challenges, and Open Questions. Under review.

Holtdirk, T., Ahnert, G., Sakshaug, J. W., & Haensch, A.-C. (2026). In-Context Learning for the Imputation of Public Opinion Data with Large Language Models. arXiv:2606.09351.

Zhang, Y., Ma, B., Cao, Y., Wu, C., Hershcovich, D., & Haensch, A.-C. (2026). ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China. arXiv:2606.08959.

Books

Kohler, U., Kreuter, F., & Haensch, A.-C. (2026). Data Analysis Using Stata (4th ed.). College Station, TX: Stata Press.

Haensch, A.-C., Feder, B., Lane, J., Tombari, A., & Kreuter, F. (2025). Data Literacy and Evidence Building. Leanpub.

Haensch, A.-C. (2021). Dealing with Various Flavors of Missing Data in Ex-Post Survey Harmonization and Beyond. Doctoral dissertation, University of Mannheim.

Book Chapters

Weiß, B., Schulz, S., Schmid, L., Sterl, S., & Haensch, A.-C. (2024). Harmonizing and Synthesizing Partnership Histories from Different German Survey Infrastructures. In I. Tomescu-Dubrow, C. Wolf, K. M. Slomczynski, & J. C. Jenkins (Eds.), Survey Data Harmonization in the Social Sciences. Wiley.

Haensch, A.-C., Beck, J., & Kreuter, F. (2022). Die COVID-19 Trends and Impact Surveys. In B. Wawrzyniak & M. Herter (Eds.), Neue Dimensionen in Data Science. VDE Verlag.

Haensch, A.-C., Stöckinger, C., & Stingl, D. (2020). Schluss mit Sterne gucken: Frequentistische Alternativen zum p-Wert. In R. Kluge & F. Meinfelder (Eds.), Bad Science: Die dunkle Seite der Statistik. Vahlen.

Other Publications

Clinton, J. D., Barari, S., Busby, E., Buskirk, T. D., Duch, R., Haensch, A.-C., Hillygus, D. S., Kennedy, C., Munger, K., Rivers, D., & Westwood, S. (2026). Public Opinion in the Age of AI. Report of the APSA Presidential Task Force on AI, Politics, and Political Science. APSA Preprints.

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

Haensch, A.-C., Drechsler, J., & Bernhard, S. (2020). TippingSens: An R Shiny Application to Facilitate Sensitivity Analysis for Causal Inference Under Confounding. IAB-Discussion Paper 29/2020.

Haensch, A.-C., Schulz, S., Sterl, S., & Weiß, B. (2019). The HaSpaD Project. Harmonization Newsletter, 5(1), 20–21.

Haensch, A.-C. (2014). Die Effekte von Koalitionspräferenzen und -erwartungen auf Wahlentscheidungen in Verhältniswahlsystemen. Münchener Beiträge zur Politikwissenschaft.

Data Products and Software

Haensch, A.-C., Kreuter, F., et al. (2024). Pandemic Recovery Survey. GESIS, Cologne. Data file version 1.0.0.

Haensch, A.-C. (2023). “Syntucky” Synthetic Data for the Data Literacy & Evidence Building Class. NYU/Accenture/UMD/KYStats/Coleridge Initiative.

CTIS UMD Team at UMD and Facebook (2022). Global COVID-19 Trends and Impact Survey. University of Maryland Social Data Science Center.

Haensch, A.-C., & Xiong, Y. CTIS: R interface to the Global COVID-19 Trends and Impact Survey microdata and open data API.

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Talks

Selected Invited Talks

• AI for Survey Design: Generating and Evaluating Survey Questions with LLMs (with Anna Fuchs). Workshop “Shaping the Future of AI-Enabled Surveys”, University of Mannheim, 2026.
• Panelist, Artificial Intelligence and the Future of Survey Research. RTI International & Washington Statistical Society, 2026.
• Applications of Machine Learning and Artificial Intelligence in Sample Surveys. Indian Statistical Institute Platinum Jubilee Conference, 2025.
• TikTok Data for Social Science Research. University of Mannheim, 2025.
• Can Large Language Models Predict How People Vote? Evidence from Germany. GESIS Lecture Series, 2023.

Full list of invited talks and conference presentations

Invited Talks

• “AI for Survey Design: Generating and Evaluating Survey Questions with Large Language Models” (with Anna Fuchs). Workshop Shaping the Future of AI-Enabled Surveys, University of Mannheim, June 2026.
• Panelist, “Artificial Intelligence and the Future of Survey Research.” RTI International & Washington Statistical Society webinar, 2026.
• “Applications of Machine Learning and Artificial Intelligence in Sample Surveys.” Indian Statistical Institute Platinum Jubilee Conference, September 2025.
• “TikTok Data for Social Science Research: Access, Limitations, and Applications.” Computational Social Science: AI and Society, University of Mannheim, May 2025.
• “Vertrauenswürdige KI: Warum ist das wichtig und worauf kommt es an?” (with Tobias Schaeffter). Technikwissenschaftliche Klasse, BBAW, November 2024.
• “LLMs in Political and Social Science Research.” CPSS Workshop @ KONVENS, September 2024.
• “Assessing Bias in LLM-Generated Synthetic Datasets: Examining LLM Personas in German and European Elections.” DZHW S3 Meeting, June 2024.
• “Of Data, Algorithms and Humans: Generative AI as Social Science Superhero?” Long Night of the Universities, Munich, May 2024.
• “Can Large Language Models Predict How People Vote? Evidence from Germany.” GESIS Lecture Series and b-it Lecture Series, December 2023.
• “Social Media-Daten in der Forschungspraxis II: Things to Know When Working with Reddit Data.” NFDI Show & Tell, November 2022.

Selected Conference Presentations

• JSM 2025 – “TeLLMe Why (AIn’t Nothing But a Survey)? Using LLMs for Coding German Open-Ended Survey Responses on Survey Motivation.”
• JSM 2024 – Invited panelist, “Future of Statistics and Data Science in the Era of ChatGPT and LLMs.”
• IC2S2 2024; AAPOR 2023 (presented by L. von der Heyde) – “Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion.”
• BigSurv 2023 – “Seeing ChatGPT Through Students’ Eyes: An Analysis of TikTok Data.”
• ISI WSC 2023 – “Why Do They Leave? Why Do They Stay? Respondents’ Motivation in a German Mixed-Mode Panel.”
• Workshop Survey Climate and Trust in Scientific Surveys 2022 – “What Do Panelists Say About Their Own Participation Motivation?”
• pairfam 2022 (presented by S. Sterl) – “The Whole is More than the Sum of its Parts.”
• ESRA 2021 (presented by S. Schulz) – “Measuring Divorce Risk with Pooled Survey Data.”
• BigSurv 2020 – “Using Supervised Classification for Categorizing Answers to an Open-Ended Question on Panel Participation Motivation.”
• JSM 2018 – “Meta-Analysis of Survey-Based, Non-Experimental Individual Person Data with Heterogeneous Weighting Schemes.”

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Teaching & Supervision

I teach statistics, survey methodology, and social data science from the first bachelor semester to doctoral training, in German and English, in classroom, online, and flipped formats. Courses range from introductory statistics for sociology students to survey weighting, statistical disclosure control, and LLM methods for survey researchers.

Supervision

• Doctoral researchers (mentor): completed – Bolei Ma, Markus Herklotz (2026), Leah von der Heyde (2025); ongoing – Tobias Holtdirk, Anna Fuchs.
• Master’s theses: >25 at LMU Munich since 2022 (LLM applications in the social sciences, missing data, machine learning).
• Bachelor’s theses: >20 at LMU Munich since 2022; 3 in Sociology at the University of Mannheim (2021).

Full list of courses

LMU Munich – Bachelor’s level

• Statistik I für Studierende der Soziologie u.a. (lecture, 4 SWS), 2021, 2023, 2024, 2025, 2026.
• Statistik II für Studierende der Soziologie (lecture, 4 SWS), 2022, 2023, 2024; Statistik II für Sozialwissenschaftler:innen (2 SWS, two groups), 2021.
• Statistik III (lecture, co-taught), 2026/27.
• Fortgeschrittene Statistische Software (R) (1 SWS), 2022.

LMU Munich – Master’s level

• Advanced Methods in Social Data Science, 2025.
• Statistical Disclosure Control (with Jörg Drechsler), 2024.
• Kirche und Statistik (with the Faculty of Catholic Theology), 2024.

University of Maryland (JPSM) / IPSDS – Graduate level

• SURV748 Step by Step in Survey Weighting: IPSDS 2021, 2023; UMD 2024, 2026; ANU and Mannheim Business School 2024.
• SURV699M/SURV611 Review of Statistical Concepts, 2023–2026.

University of Mannheim – Bachelor’s level (Sociology)

• Quantitatives Forschungsseminar I & II: Big Data in den Sozialwissenschaften (data collection, 2 SWS; data analysis, 4 SWS), 2018–2020.
• Einführung in die Datenerhebung (2 SWS), 2017.

Short Courses and Workshops

• Fine-Tuning LLMs for Data Augmentation and Synthesis (with Tobias Holtdirk). AAPOR 2025; SurvAI Day 2024.
• Data Science Techniques for Survey Researchers. GESIS Summer School in Survey Methodology, 2023, 2024.
• FAIR Workshop: Digitale Spurendaten in der sozialwissenschaftlichen Forschung. TU Dortmund, 2024.
• Introduction to the World of Big Data & Analytics. Mannheim Business School Summer School, 2022.

Teaching Assistant

• SURV699 Introduction to Official Statistics (W. Radermacher); SURV725/726 Item Nonresponse and Multiple Imputation (J. Drechsler), IPSDS.
• Meta-Analysis in Social Research and Survey Methodology (with B. Weiß and J. Wengrzik), GESIS Summer School 2018.
• Introduction to Empirical Research Methods in Political Science (P. Thurner), LMU Munich, 2017.

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Grants, Awards & Service

Grants

• LMU Sustainability Fund, “Pilotstudie für den LMU Bodenindex” (EUR 30,000), 2026–2027.
• LMU Teaching Innovation Grant “RAINER – R Assistant IN Error Resolution” (EUR 20,000), 2024–2025.
• LMU–NYU Scholarship (EUR 10,000), 2023.

Awards

• AAPOR Burns “Bud” Roper Fellow Award, 2022.
• AAPOR Warren J. Mitofsky Innovators Award (CTIS team), 2022.
• AAPOR Policy Impact Award (CTIS team), 2022.
• Max Weber Program scholarship, 2011–2017.

Service

• Associate Editor, Survey Practice, 2026–present.
• Member, APSA Presidential Task Force on AI, Politics, and Political Science, 2025–2026.
• Member, Eurostat EMOS Board, 2024–2028.
• Junior Member, Munich Center for Machine Learning (MCML), 2024–present.
• Deputy Women’s Representative, Department of Statistics, LMU Munich, 2024–2026.
• Member, Ethics Commission, Faculty 16, LMU Munich, 2023–2025.
• Reviewer for Sociological Methodology, Public Opinion Quarterly, Sociological Methods & Research, JRSS A, Big Data & Society, Social Science Computer Review, JSSAM, Survey Research Methods, Survey Methodology, Journal of Official Statistics.

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Career

• Akademische Rätin a.Z. and Group Leader, SODA, LMU Munich, 2024–present
• Assistant Research Professor, JPSM, University of Maryland, 2021–present
• Research Associate, SODA, LMU Munich, 2021–2023
• Visiting Scholar, Wagner School of Public Service, New York University, 2023
• Postdoctoral Researcher, Institute for Employment Research (IAB), 2021
• Doctoral Researcher, University of Mannheim (2019–2020) and GESIS (HaSpaD project, 2017–2021)
• Parental leave: full-time Nov 2025–Mar 2026; part-time Apr 2026–Feb 2027

Education: PhD Sociology, University of Mannheim (2021, 1.0) · MSc Survey Statistics, University of Bamberg (2017) · BA Political Science and Sociology, LMU Munich (2014)

Languages: German (native), English (C2), French (C1), Spanish (B1) · Software: R, Python, Stata, SQL