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GESIS Training
GESIS - Leibniz-Institute for the Social Sciences

GESIS Training News

July 2023

Spring Seminar | Summer School | Fall Seminar | Workshops

Table of Contents

GESIS Summer School in Survey Methodology 2023 – Few Places Available

Take advantage of this opportunity to expand your knowledge in survey methodology, research design, data collection, and survey data analysis! Join us from 02 - 25 August 2023, as we bring together lecturers and participants from around the globe and diverse fields. Connect with like-minded individuals and experience the best of both worlds with a mix of onsite courses at GESIS Cologne and online sessions. Register now and secure your spot at the GESIS Summer School 2023.

Following, you can find an overview of this year's courses:

Week 0 (02 - 04 August) – Short Courses

Pretesting Survey Questions

Cornelia Neuert (GESIS), Timo Lenzner (GESIS)   

Mixed-Mode Surveys

Sven Stadtmüller (GESIS and Frankfurt University of Applied Sciences), Henning Silber (GESIS), Yannick Diehl (University of Marburg), Peter Schmidt (University of Giessen)   

Introduction to Stata for Data Management & Analysis

Irina Bauer (GESIS), Annika Stein (GESIS)    

Causal Inference with Directed Acyclic Graphs (DAGs)

Paul Hünermund (Copenhagen Business School)    

Week 1 (07 - 11 August)

Designing, Implementing, and Analyzing Longitudinal Surveys

Tarek Al Baghal (University of Essex), Alexandru Cernat (University of Manchester)   

Applied Systematic Review and Meta-Analysis

Jessica Daikeler (GESIS), Sonila Dardha (Meta)    

Causal Inference Using Survey Data

Heinz Leitgöb (Leipzig University), Tobias Wolbring (FAU Erlangen-Nürnberg)    

(Non-)Probability Samples in the Social Sciences

Carina Cornesse (German Institute for Economic Research Berlin, DIW and University of Bremen), Olga Maslovskaya (University of Southampton)   

Week 2 (14 - 18 August)

Advanced Survey Design

Bella Struminskaya (University of Utrecht), Angelo Moretti (University of Utrecht)

Advanced Questionnaire Design

Marek Fuchs (Darmstadt University of Technology)    

Introduction to R for Data Analysis

Jan Schwalbach (GESIS), Dennis Abel (GESIS)    

Missing Data and Multiple Imputation

Florian Meinfelder (University of Bamberg), Angelina Hammon (German Institute for Economic Research Berlin, DIW and University of Bamberg)    

Week 3 (21 - 25 August)

Collecting and Analyzing Longitudinal Social Network Data

Lars Leszczensky (University of Mannheim), Sebastian Pink (University of Mannheim)   

Data Science Techniques for Survey Researchers

Anna-Carolina Haensch (LMU Munich and University of Maryland)   

ECTS Credits & More

Thanks to our cooperation with the Center for Doctoral Studies in Social and Behavioral Sciences at the University of Mannheim, participants can obtain a certificate acknowledging a workload worth 4 ECTS credit points per one-week course. More information is available here.

There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. You will find the full program, detailed course descriptions, and more information here.

GESIS Workshops 2023 – Tailored to Your Needs

Watch out for our expanded workshop program! Besides our popular courses on qualitative (qualitative network analysis, content analysis, grounded theory methodology), and quantitative research methods (multilevel analysis, sequence analysis), we added the following new workshops to our program:

For further information and registration, please visit the workshop website or check the list with the entire program below.

GESIS Fall Seminar in Computational Social Science 2023 – Still Places Available

Join us in Mannheim from 11 to 29 September and immerse yourself in a wide range of introductory and advanced courses on computational social science methods. The Fall Seminar is designed for researchers from the social sciences, data science, communication science, and the digital humanities who are eager to collect and analyze data from the web, social media, or digital text archives. Led by experts from GESIS and worldwide, the courses delve into the methods and techniques of working with digital behavioral data (aka "big data").

Participants can choose from nine week-long courses, including introductions to Computational Social Science, Web Data Collection, Big Data Management, and Machine Learning. For those seeking more specialized knowledge, there are courses on Automated Image and Video Data Analysis, Deep Learning for Advanced Computational Text Analysis, and Network Analysis. Each course combines insightful lectures with hands-on exercises, allowing participants to apply these methods to real data. Don't miss this opportunity to enhance your skills and stay at the forefront of computational social science! Register now!

Below, you can find an overview of this year's courses:

Week 1 (11 - 15 September)

Introduction to Computational Social Science with R

Aleksandra Urman (University of Zurich), Max Pellert (University of Mannheim)    

Introduction to Computational Social Science with Python

Milena Tsvetkova (London School of Economics), Patrick Gildersleve (London School of Economics)    

Big Data and Computation for Social Data Science

Akitaka Matsuo (University of Essex), David (Yen-Chieh) Liao (Aarhus University)

Week 2 (18 - 22 September)

Automated Web Data Collection with R

Allison Koh (Hertie School of Governance), Hauke Licht (University of Cologne)

Automated Web Data Collection with Python

Felix Soldner (GESIS), Jun Sun (GESIS), Leon Fröhling (GESIS)    

Automated Image and Video Data Analysis with Python

Andreu Casas (Vrije Universiteit Amsterdam), Felicia Loecherbach (New York University)

Week 3 (25 - 29 September)

Social Network Analysis with R

Michał Bojanowski (Kozminski University and Universitat Autònoma de Barcelona)    

Introduction to Machine Learning for Text Analysis with Python

Damian Trilling (University of Amsterdam), Anne Kroon (University of Amsterdam)

From Embeddings to Transformers: Advanced Text Analysis with Python

Hauke Licht (University of Cologne), Jennifer Victoria Scurrell (ETH Zurich)

For those without any prior experience in R or Python and those who’d like a refresher, we’re additionally offering two pre-courses, "Introduction to R" and "Introduction to Python" (three days, online) in the week before the start of the Fall Seminar.

All courses are stand-alone and can be booked separately – feel free to mix and match to build your own personal Fall Seminar experience that perfectly suits your needs and interests. There is no registration deadline, but places are limited and allocated on a first-come, first-served basis. To secure a place in the course(s) of your choice, we strongly recommend that you register early.

Thanks to our cooperation with the a.r.t.e.s. Graduate School for the Humanities at the University of Cologne, participants can obtain a certificate acknowledging a workload worth 2 ECTS credit points per one-week course. More information is available here.

For detailed course descriptions and registration, please visit our website and sign up here!

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

Mark your calendars for 26 February to 15 March 2024! Get ready to embark on a journey of learning and innovation. 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 reflects the latest developments and innovations in social science research methodology. Next year's topic will be "Recent Developments in Longitudinal Data Analysis".

By attending the Spring Seminar, you can expect the following:

🔍 In-depth knowledge of state-of-the-art quantitative research methods in the social sciences.

💡 Application of cutting-edge quantitative analysis techniques to your own research projects.

🤝 Engaging discussions with interested colleagues and leading experts.

🌐 Networking opportunities in a supportive social environment.

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 in hybrid mode. Don't miss out on this enriching experience! Stay tuned for more information here.

Interview with Michał Bojanowski (Kozminski University & Autonomous University of Barcelona)


Michał Bojanowski is an assistant professor at the Chair of Quantitative Methods and Information Technology at Kozminski University and a researcher at the COALESCE Lab at the Autonomous University of Barcelona. He holds a PhD in sociology (2012, ICS / Utrecht University), and his research focuses on modeling social network data, especially those collected with non-sociocentric designs, as well as on assembling complex social network datasets from non-obvious sources (such as historical archives) often using technically-advanced procedures. Michał is an R developer with over 20 years of experience in writing packages and providing training in academic and business contexts. He is a member of the Statnet Development Team, maintaining a suite of R packages for statistical network analysis.

He will teach the course "Social Network Analysis with R" at the Fall Seminar in September 2023.

How did you become interested in your subject?

Michał: My interest in network analysis came to be by means of a kind-of a "Bang!" in the early 2000s, two years after I graduated with MA in Sociology from University of Warsaw. I knew very little about networks then, and even less about their relevance for social science in general. By the "Bang!" looking back now, I mean a couple of things came together somewhat unexpectedly. On the one hand, my interest and relatively strong skills in data analysis. On the other hand, a theoretical void or dissatisfaction with social scientific theories. The third element, a catalyst, or an event rather, was me coming across research by Vincent Buskens on trust and social networks. He was using game theory to derive testable sociological hypotheses. I was fascinated because I knew game theory a little bit but treated it as abstract math prescribing how to act rationally in interactive situations, not an approach that can be used to explain observable phenomena. By effort and luck, a year and a half later, Vincent became my PhD supervisor at the ICS at Utrecht University, and I was making a deep dive into the world of social networks, not without the help of Stephanie Rosenkranz and Werner Raub, who complemented the advisory team. Everything was exciting, new, and challenging.

Of all the fascinating instances in which networks can be studied I was always finding network formation questions the most interesting, i.e.: why and how various types of social ties come into existence. I mean here ties in social systems in which, for example, people compete with some set of actors but collaborate with a different set — a situation not unlike between scientists. Or pursue forming relations with others, but yet would like others not to do the same — not unlike adolescents competing for friendship and attention of peers. The sociological question is then what kind of structures of social networks (e.g., in science or school class) we should expect from such lower-level processes. That's fascinating! Even more so if we acknowledge that each tie, when you think of it, represents a relation initiated privately, or even intimately, by a pair of social actors, but when looked at in their totality in a larger population, combine into a large and complex network, which constitutes a social context, thus an antecedent, for the creation of that very tie in the first place.

What lessons can participants draw from your GESIS course?

Michał: I intend to have everybody finish the course feeling empowered and enabled in several ways. Firstly, the participants should feel enabled to perform their own social network analytic project using R by themselves, comprising all the typical stages: import and transformation of network data, descriptive measures giving a sense of structural features, visualization, and perhaps some elements of statistical analysis. Still, a week-long course might not be enough for everybody to get to that level because SNA is a rich paradigm, and the set of tools for SNA in R is big and growing. Thus the secondary but equally important skill is to know how to learn on their own, e.g., how to start using an unfamiliar R function, how to search and read documentation, how to interpret error messages, where to look for help and additional information, and so on. By experience, I think this is an often overlooked aspect of R training, even though R seems unwelcoming to many just because of that. I hope we will disenchant R on that front during the course. Thirdly, I hope the course will broaden participants' social network science imagination in the sense of possible research questions and mechanisms that may hide behind their network data, which they perhaps did not consider in their research.

What do you enjoy most about being a social scientist?

Michał: I think, being a curiosity-driven person, I would say I enjoy the most the breadth of research problems out there, ready to be pursued, and the possibility of jumping between them. I don't think this freedom is achievable in any other profession. Another related aspect I enjoy a lot is interdisciplinarity which enables me to find a common language with, say, statisticians, historians, or lawyers with whom I collaborate on a couple of distinct projects currently.

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

Michał: There are many exciting things happening currently, even more so because network science attracts researchers from many different fields. I think the most exciting developments are in the statistical models of networks: static, dynamic, multiplex, and so on. The new methods and approaches (such as Exponential-family Graph Models and their extensions or Stochastic Actor-Oriented Models and their extensions) open various new possibilities. One example is the ability to express multiple theoretical ideas, new and classic ones, e.g., about network multiplexity, and rigorously test them against data. Another example is the potential of applying these methods to new datasets that are becoming available in areas beyond core social science, such as history or archaeology.

We thank Michał for his exciting insights and look forward to his course.

Training Courses in English

04-06/09/23OnlineIntroduction to Python
(Hannah Béchara, Paulina Garcia Corral)
05-07/09/23OnlineIntroduction to R
(Natalia Umansky, Christian Pipal)
30-31/10/23MannheimFundamentals and Advanced Topics in Modeling Interaction Effects
(Janina Beiser-McGrath, Liam F. Beiser-McGrath)
06-08/11/23OnlineSequence Analysis in the Social Sciences
(Marcel Raab, Emanuela Struffolino)
13-14/11/23MannheimQuestionnaires for Cross-Cultural and Cross-National Surveys
(Dorothée Behr, Cornelia Neuert, Lydia Repke)
13-14/11/23OnlinePower Analysis Through Simulation in R
(Niklas Johannes)
13-15/11/23OnlineSynthetic Data: Generation and Evaluation
(Thom Volker)
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)
20-21/02/24OnlinePropensity Score Matching: Computation and Balance Estimation for two and more groups in R
(Julian Urban)
20-22/03/24OnlineApplied Multiverse Analysis
(Reinhard Schunck)

Training Courses in German

07-08/08/23MannheimQualitative Interviews – Theorie und Praxis
(Günter Mey, Paul S. Ruppel)
(Günter Mey, Paul S. Ruppel)
29-31/08/23CologneQualitative Netzwerkanalyse
(Laura Behrmann, Markus Gamper)
30/08-01/09/23CologneMehrebenenanalyse in Stata
(Hermann Dülmer)
28-29/09/23CologneEinführung in die qualitative Inhaltsanalyse
(Markus Janssen, Christoph Stamann)
29/11-01/12/23CologneEinführung in die Strukturgleichungsmodellierung
(Marie-Ann Sengewald)
21-23/02/24CologneDurchführung qualitativer Interviews
(Nicole Bögelein, Katharina Leimbach)
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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