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GESIS Training
GESIS - Leibniz-Institute for the Social Sciences

GESIS Training News

October 2023

Spring Seminar | Summer School | Fall Seminar | Workshops

Table of Contents

GESIS Workshops 2024 – Tailored to Your Needs

GESIS Spring Seminar 2024 – Cutting-Edge Methods – Save the Date

Save the date: 26 February - 15 March 2024, for an inspiring learning experience! The GESIS Spring Seminar is renowned for providing high-quality training in state-of-the-art techniques in quantitative data analysis taught by leading experts in the field. Each year, we select a topic that mirrors the latest advancements and innovations in social science research methodology. Next year we’ll delve into "Recent Developments in Longitudinal Data Analysis". We are excited to announce that Scott Cunningham, author of Causal Inference: The Mixtape, will be teaching a class on "Recent Developments in Difference-in-Differences Estimation" in week 2 (04 - 08 March). In week 3 (11 - 15 March), Martin Spindler will cover "Causal Machine Learning for Cross-sectional and Panel Data". The course in week 1 will focus on "Advanced Longitudinal and Panel Data Analysis". More details to follow soon.

Participate in the Spring Seminar to ...

🔍 gain profound insights into advanced quantitative research methods in the social sciences;

🔬 apply the latest quantitative analysis techniques to enhance your research endeavors;

🗣️ engage in stimulating conversations with like-minded peers and esteemed professionals;

🤝 expand your network within a welcoming social setting.

The Spring Seminar is tailored for advanced graduate or PhD students, post-docs, and junior and senior researchers who are eager to enhance their knowledge and skills in state-of-the-art techniques in quantitative data analysis. Each course combines insightful lectures with hands-on exercises, providing you with the opportunity to apply these methods to real data. The courses will be taught onsite at GESIS Cologne or offer both onsite and online participation (hybrid). Don't miss out on this enriching experience! Stay tuned for more information here.

A Look Back – GESIS Summer School in Survey Methodology 2023

For the twelfth time in a row, Europe’s leading summer school in survey methodology took place from 02 - 25 August 2023 in Cologne and online. Over 200 participants from the international academic community took part in a series of excellent virtual and onsite courses on methods and techniques of survey methodology. [Continue reading]

If you could not make it this summer, please save the date for the next edition:

24 July - 16 August 2024!

The courses will take place either at GESIS Cologne, online via Zoom, or offer both onsite and online participation (hybrid). The program will be announced in February 2024 and in our newsletter in the first quarter of 2024. For more information on the GESIS Summer School, visit our website.

Stay tuned!

Fabienne Lind (University of Vienna) & Hauke Licht (University of Cologne)


Fabienne Lind is a postdoctoral researcher at the Department of Communication at the University of Vienna. Her main research interests are the advancement of content analysis methodology, comparative research, European media discourses about migration, and knowledge gap research. In her dissertation, she introduces, compares, and evaluates strategies for the automated analysis of text collections in different languages for cross-country research. Fabienne Lind was part of the Horizon 2020 projects REMINDER and MIRROR and is currently a member of the H2020 project OPTED and the COST Action OPINION.


Hauke Licht is a postdoctoral researcher at the Cologne Center for Comparative Politics, University of Cologne, who received his PhD from the University of Zurich. He develops and applies computational text analysis methods to study political communication, electoral competition, and democratic representation. Taking a comparative perspective, Hauke often analyzes multilingual texts and thus has a strong interest in multilingual text analysis methods. He also increasingly uses deep learning methods to analyze textual and audio-visual data.

They will teach the course "Going Cross-Lingual: Computational Methods for Multilingual Text Analysis" in Cologne in December 2023.

How did you become interested in your subject?

Fabienne: I became interested in multilingual text analysis when I was involved in a project that aimed to analyze migration reporting in Europe. What truly fascinated me was the challenge of working with texts in different languages and attempting to compare them. Indeed, there were already some approaches to making these comparisons possible through human interpretation and analysis of data. However, in the realm of automated analysis, which was still relatively new in the field of social sciences at that time, there was a lack of clear guidance and established methods. This presented an exciting and challenging opportunity to explore innovative techniques and contribute to the evolution of the field.

Hauke: Like Fabienne, I also came to the topic as a practitioner. I wanted to study to what extent and under what conditions political parties criticize “the elite” as part of their electoral strategies. But to implement a suitable comparative research design, I needed to measure parties’ emphasis on anti-elite rhetoric across countries and hence languages. I was aware of the option of automated (“machine”) translation to “solve” this problem. In this case, I could just have applied established text-as-data approaches to texts’ English translations. But when I calculated how much it would cost to translate all texts in our dataset using Google Translate, DeepL, or another commercial service, I was shocked. So I started to look for alternatives across the political, communication, and computer science literature.

What lessons can participants draw from your GESIS course?

Fabienne & Hauke: One of the core takeaways from this course is the importance of collaboration with case and language experts when undertaking multilingual text analysis projects. For instance, when analyzing social media data from various regions, having a local linguist who understands the nuances of regional slang or dialects can significantly enhance the accuracy of sentiment analysis. Collaboration with experts ensures that the context and cultural nuances of the text are adequately considered, which can make a substantial difference in the project's outcomes.

Moreover, another takeaway is that numerous tools and resources are readily available to facilitate multilingual text analysis projects. We will provide participants with the knowledge and hands-on experience to harness these tools effectively in their projects, streamlining the analysis process and enhancing the quality of their research.

What do you enjoy most about being a social scientist?

Fabienne: Being a social scientist for me is like using a mirror to understand how society changes over time. The mirror isn't perfect, and what it shows is like a constantly moving puzzle. But I really enjoy the challenge of trying to figure out some of the mysteries.

What do you think is the most exciting recent development in your field?

Hauke: The aspect of research about multilingual text analysis I find most fascinating is its level of interdisciplinarity. Today, social, communication, and political scientists are increasingly aware of the opportunities of multilingual text analysis. Researchers from all across social science seem to realize that analyzing texts across languages can allow comparative insights into communication and political behavior and help understanding how context and culture shape discourse more generally. I believe that this development is in large parts driven by methodological research that expands our theoretical knowledge about multilingual text analysis as well as our methodological toolkit. What’s striking is that while many of these contributions come from social science researchers, they often import and adapt insights and methods developed in (computational) linguistics as well as from neighboring fields within the social sciences. This makes research on multilingual text analysis in the social sciences extremely interdisciplinary.

We thank Fabienne and Hauke for their exciting insights and look forward to their course.

Training Courses in English

06-08/11/23OnlineSequence Analysis in the Social Sciences
(Marcel Raab, Emanuela Struffolino)
13-14/11/23OnlinePower Analysis Through Simulation in R
(Niklas Johannes)
16-17/11/23OnlineWorkflows for Reproducible Research with R & Git
(Johannes Breuer, Bernd Weiß, Arnim Bleier)
22-24/11/23OnlineIntroduction to Bayesian Statistics
(Denis Cohen)
28-30/11/23OnlineResearch Data Management and Open Science
(Anja Perry, Sebastian Netscher)
06-08/12/23CologneGoing Cross-Lingual: Computational Methods for Multilingual Text Analysis
(Hauke Licht, Fabienne Lind)
07-08/12 & 14-15/12/23Online Introduction to Event History Analysis
(Jan Skopek)
11-15/12/23OnlineCausal Mediation Analysis
(Felix Thoemmes)
13-15/12/23OnlineIntroduction to Stata
(Alexandra Asimov, Katrin Firl)
16-18/01/24OnlineIntroduction to R for Quantitative Social Science
(Ranjit Konrad Singh, Björn Rohr)
18-19/01/24MannheimData Quality Assessment for Online Survey Responses: Be Careful of the Careless
(Thomas Knopf, Matthias Roth)
29/01-01/02/24CologneDecomposition Methods in the Social Sciences
(Johannes Giesecke, Ben Jann)
14-15/02/24OnlineAutomatic Sampling and Analysis of YouTube Data
(Johannes Breuer, Rohangis Mohseni, Annika Deubel)
20-21/02/24OnlinePropensity Score Matching: Computation and Balance Estimation for two and more groups in R
(Julian Urban)
20-23/02/24OnlineApplied Machine Learning with R
(Paul C. Bauer)
13-15/03/24OnlineIntroduction to Computational Text Analysis with R
(Lea Kaftan, Jan Schwalbach)
20-22/03/24OnlineApplied Multiverse Analysis
(Johanna Pauliks, Reinhard Schunck)
21-22/03/24OnlineUsing Smartphone Sensors, Apps, and Wearables
(Bella Struminskaya, Florian Keusch)
11-12/04/24OnlineIntroduction to Quantile Regression
(Andreas Haupt, Sebastian E. Wenz)
23-24/04/24CologneIntroduction to Geospatial Techniques for Social Scientists in R
(Stefan Jünger, Anne-Kathrin Stroppe)
24-26/04/24OnlineIntroduction to Deep Learning in R
(Christian Arnold)
06-08/05/24OnlineAdvanced Bayesian Statistical Modeling in R and Stan
(Denis Cohen)
14-17/05/24OnlineApplied Data Visualization with R
(Paul C. Bauer)
01-03/07/24MannheimGeodata and Spatial Regression Analysis
(Tobias Rüttenauer)
12-14/11/24CologneMixed Methods und Multimethod Research (MMMR)
(Andrea Hense)

Training Courses in German

29/11-01/12/23CologneEinführung in Strukturgleichungsmodellierung
(Marie-Ann Sengewald)
21-23/02/24CologneDurchführung qualitativer Interviews
(Nicole Bögelein, Katharina Leimbach)
(Günter Mey, Paul Sebastian Ruppel)
(Laura Behrmann, Nicole Bögelein)
19-21/06/24MannheimGrundlagen und aktuelle Debatten der Regressionsanalyse
(Michael Gebel, Stefanie Heyne)
03-05/09/24CologneQualitative Netzwerkanalyse
(Laura Behrmann, Markus Gamper)
GESIS – Leibniz Institute for the Social Sciences, Department Knowledge Exchange & Outreach, GESIS Training, P.O. Box 12 21 55, 68072 Mannheim, training@gesis.org
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