GESIS Training News

The Fall Seminar takes place from 30 August to 27 September 2024 and offers a variety of introductory and advanced courses in computational social science methods. It targets researchers who want to collect and analyze data from the web, social media, or digital text archives. All courses are taught in English and lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data. Some courses are held in-person in Mannheim, others online – keep an eye out for our brand-new blended learning format!

Please find our full course program below:

Introduction to Computational Social Science with R

[30 August-05 September | online blended learning]

Johannes B. Gruber (University of Amsterdam)   

Introduction to Computational Social Science with Python

[30 August-05 September | online blended learning]

John McLevey (Waterloo University)   

Web Data Collection with Python and R

[09-13 September | Mannheim]

Iulia Cioroianu (University of Bath)   

Introduction to Social Network Analysis

[16-20 September | Mannheim]

Philip Leifeld (University of Manchester)   

Introduction to Machine Learning for Text Analysis with Python

[16-20 September | Mannheim]

Marieke van Hoof (University of Amsterdam), Rupert Kiddle (Vrije Universiteit Amsterdam)   

Agent-Based Computational Modeling

[16-20 September | Mannheim]

Michael Mäs (Karlsruhe Institute of Technology), Fabio Sartori (Karlsruhe Institute of Technology)   

Advanced Social Network Analysis with R

[23-27 September | Mannheim]

Michal Bojanowski (Kozminski University & Autonomous University of Barcelona)   

From Embeddings to LLMs: Advanced Text Analysis with Python

[23-27 September | Mannheim]

Hauke Licht (University of Cologne), Lisa Maria Lechner (University of Innsbruck)   

Automated Image and Video Data Analysis with Python

[23-27 September | online]

Andreu Casas (Royal Holloway University of London), Felicia Loecherbach (University of Amsterdam)   

ECTS Credits & More

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.

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 book early.

For detailed course descriptions and registration, please visit our website.

Boost your skills with our summer workshops, featuring a variety of innovative methods! ☀️ 🚀

Discover Geodata and Spatial Regression Analysis and learn advanced programming in Data Management, Advanced Programming and Automation using Stata and Interactive Data Analysis with Shiny. If you’re just getting started with R or Python, then our Introduction to R and Introduction to Python workshops might be what you’re looking for.

If you want to learn how to design and perform simulation studies, Using Simulation Studies to Evaluate Statistical Methods offers the ideal course for you. If you specialize in the life course research, then Sequence Analysis in the Social Sciences is the right choice. If you are into longer-term planning and are intrigued by applied Bayesian modeling, Introduction to Bayesian Statistics would be the perfect fit. This approach has gained popularity in the social sciences and beyond.

Moreover, get ready for our newest lineup of workshops including:

• Advanced Bayesian Statistical Modeling in R and Stan
• Time Series Analysis for Modeling Intensive Longitudinal Data
• Introduction to Longitudinal Structural Equation Modeling

Participate in the workshops to ...

🔍 Deepen your understanding of quantitative and qualitative research methods in the social sciences;

🔬 Apply state-of-the-art data collection and analysis techniques to your research;

🗣️ Make use of the opportunity to directly engage with your lecturers to clarify questions and receive feedback.

For additional details, registration, and our complete workshop program, visit our workshop website or have a look at the complete program below.

The GESIS Summer School 2024 is scheduled from 24 July to 16 August 2024. Some courses will be conducted onsite in Cologne, while others will be held online. Join lecturers and attendees from diverse backgrounds and locations worldwide, both in-person and virtually, to participate in Europe's leading summer school in survey methodology, research design, and data collection.

Below, you'll find an outline of this year's courses:

Week 0 (24–26 July) – Short Courses

Introduction to R for Data Analysis [24–25 July]

Jan Schwalbach (GESIS), Dennis Abel (GESIS)   

Developing, Translating, and Pretesting Questionnaires for Cross-cultural Surveys

Dorothée Behr (GESIS), Cornelia Neuert (GESIS), Lydia Repke (GESIS)   

Introduction to Stata for Data Management & Analysis

Mazlum Karatas (GESIS), Lynn-Malou Lutz (GESIS)   

Week 1 (29 July–02 August)

Factorial Survey Design

Katrin Auspurg (LMU Munich), Carsten Sauer (Bielefeld University), Alisia Bauer (LMU Munich)   

Introduction to Questionnaire Design

Marek Fuchs (Darmstadt University of Technology)   

Designing and Implementing Web Surveys

Melanie Revilla (IBEI)    

Causal Inference with Directed Acyclic Graphs (DAGs) [31 July–02 August]

Paul Hünermund (Copenhagen Business School)   

Week 2 (05–09 August)

Causal Inference Using Survey Data

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

Survey Sampling and Weighting

Simon Kühne (Bielefeld University)   

Designing, Implementing, and Analyzing Longitudinal Surveys

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

Mixed-Mode Surveys [07–09 August]

Henning Silber (GESIS), Sven Stadtmüller (HAWK Göttingen), Peter Schmidt (University of Giessen), Yannick Diehl (University of Marburg)    

Week 3 (12–16 August)

Introduction to Survey Design

Bella Struminskaya (University of Utrecht), Peter Lugtig (University of Utrecht)    

Data Science Techniques for Survey Researchers

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

Introduction to Small Area Estimation

Angelo Moretti (University of Utrecht)   

Applied Multiple Imputation

Ferdinand Geißler (Humboldt-University Berlin), Jan Paul Heisig (WZB Berlin and Freie Universität Berlin)    

Scholarships, ECTS Credits, & More

The deadline to apply for one of four scholarships sponsored by the European Survey Research Association (ESRA) has passed. 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.

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

Johannes B. Gruber is a Postdoctoral Researcher at the Department of Communication Science at the University of Amsterdam. His research explores the flow of information through media systems containing news, social, and alternative media within the NEWSFLOWS project. Previously, he worked at the Vrije Universiteit Amsterdam developing open-source research software within the OPTED project. He has developed and contributed to software packages for web scraping (traktok, paperboy, cookiemonster), text analysis (spacyr, quanteda.textmodels, stringdist, rwhatsapp, LexisNexisTools), and data storage (amcat4r). In his PhD, obtained in 2021 in Politics at the University of Glasgow, he studied the portrayal of protest events in UK mainstream news media.

He will teach the course "Introduction to Computational Social Science with R" at the Fall Seminar. The blended learning course will take place from 30 August-05 September.

How did you become interested in your subject?

Johannes: I've always been a bit of a computer nerd. But when I chose a subject to study, I looked at the computer science degrees and couldn't imagine fighting through all the maths, while what I really wanted to understand is how our society functions. Fast-forward to the end of my Political Science MSc and the last session blew me away when my teacher showed off his newest research using text-as-data methods. I was immediately hooked by the idea that the computer could be used to analyse traces of political and social phenomena. The vast amounts of data produced by parliaments, institutions, news media, and social media users suddenly did not seem like inextricable chaos anymore, but treasure troves for social science research. And it was accessible just with my computer instead of big pots of funding or an army of volunteers!

After working through some text books and teaching myself R along the way that summer, I decided that combining the two things I was most interested in - society and computers - was what I wanted to do as a career. I'm pretty sure that the skill I picked up then and refined afterwards are what got me a PhD scholarship and several amazing Postdoc positions afterwards.

What lessons can participants draw from your GESIS course?

Johannes: I hope that the participants of my course will look at a dataset or social phenomenon one day and think: hey, I know which methods I can use to study this! The course gives a broad overview of ways to obtain data and extract insights from it. This starts with web scraping and API access, we cover text and network analysis before getting into agent based modelling. At the end of the course, I want participants to know what is out there and where to look for approaches and software. But most importantly, I hope that people get creative with the methods and find clever solutions. This is what I always find the most interesting research in Computational Social Science. Often, it is hard to find out what people did and why by asking them. Because the relevant people are not accessible, people do not have a detailed recollection of what they did when or there are just too many people you would need to talk to in order to see the pattern. Yet, by combining knowledge about digital traces of human behaviours, we might be able to understand things like how news and misinformation spread on social media or how the censorship of a regime works.

What do you enjoy most about being a social scientist?

Johannes: The range of tasks that this brings with it and the new knowledge that could be garnered. Social science research is often like detective work, where you need to know a wide variety of things about the world, but also have some idea about how people behave. But unlike the detective, we're not merely trying to guess and then prove what a criminal did, we get to actually find out something that nobody has ever known before.

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