Welcome! I am passionate about research on missing data, synthetic data, and big data in the social sciences. My work spans Statistics, Computational Social Science, and NLP, and I publish in traditional statistics and methods journals and NLP conference proceedings. 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

Publications

Current working papers and book projects

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

von der Heyde, L., Haensch, A., & Wenz, A. (2024). Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion. Under review.

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

von der Heyde, L., Haensch, A., & Wenz, A. (2024). United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections. Under 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.-C., Kreuter, F., Weber, W., Zins, S., La Motte-Kerr, W., Li, Y., Stewart, K., Gakidou, E., & Mokdad, A. H. (2024). Insights on Pandemic Recovery: A Comprehensive Analysis from a 21-Country Online Survey. Under Review.

Sommer, F., Schade, R., Prokosch, D., Coelho, I. B., Haensch, A.-C., & Kreuter, F. (2024). Survey on the Effectiveness of the Rent Brake in Munich. Submitted to arXiv.

Published as books, in journals (Statistics and CSS) or conference proceedings (NLP)

Haensch, A.-C., Feder, B., Lane, J., Tombari, A., & Kreuter, F. (2024): Data Literacy and Evidence Building. Leanpub. leanpub.com/dlev

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. Accepted at EMNLP Findings 2024.

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.

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

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. 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. 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. 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).

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.

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.<be

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)

“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 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 2024.
“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 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) 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