If you can't see this message, view it in your browser.
GESIS Training
GESIS - Leibniz-Institute for the Social Sciences

GESIS Training News

April 2023

Spring Seminar | Summer School | Fall Seminar | Workshops

Table of Contents

GESIS Fall Seminar in Computational Social Science 2023 – Registration is Open

We are excited to announce the program of the Fall Seminar in Computational Social Science 2023: Join us in Mannheim from 11 to 29 September and choose from a variety of introductory and advanced courses on computational social science methods! The Fall Seminar targets social scientists, data scientists, and researchers in the digital humanities that want to collect and analyze data from the web, social media, or digital text archives. Its courses are taught by both GESIS and international experts and cover methods and techniques of working with digital behavioral data (“big data”).

Participants can pick from nine week-long courses, including introductory courses on Computational Social Science, Web Data Collection, Big Data Management, or Machine Learning, and more specialized topics such as Automated Image and Video Data Analysis, Deep Learning for Advanced Computational Text Analysis, or Network Analysis. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data. All courses are held in English.

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 Workshops 2023 – Tailored to Your Needs

R is turning 30 this year! If this isn't the perfect opportunity to learn R or deepen your R skills, we don't know when is. Besides an introduction to R, we offer exciting workshops on data visualization, functional programming, debugging, and package writing, reproducible documents with Markdown and Quarto, and interactive data analysis with Shiny in the coming months.

After toasting to R, we focus on the working horse of data analysis in June and July: regression models. We start with an introduction to regression analysis, followed by advanced techniques for categorical dependent variables, and spatial data. If you want to use the latter and don't know how, look at our intro to geospatial techniques for social scientists.

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

GESIS Summer School in Survey Methodology 2023 – Places Available

The Summer School 2023 will take place from 02 to 25 August 2023. Some courses will be held onsite at GESIS Cologne and some online. Join lecturers and participants from all over the world and from many different fields in meeting in person and online to take part in Europe's leading summer school in survey methodology, research design, and data collection.

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.

Interview with Florian Foos (London School of Economics)


Florian Foos is an Associate Professor in Political Behaviour in the Department of Government at the LSE. As a field experimentalist, he studies how offline and online interactions between political actors and voters affect mobilization, opinion change, and political activism. He is a member of Evidence in Governance and Politics (EGAP) and teaches widely on causal inference and field experimentation.

He will teach the course “Social Media-Based Field Experiments” with Asli Unan in July 2023.

How did you become interested in your subject?

Florian: I became interested in causal inference and field experiments as a Master's student 12 years ago (that's a long time ago …!). Not many political scientists were doing field experiments in Europe at the time. Peter John, who I luckily met at a conference two years later, was one of them. But two brilliant postdocs who happened to be in the same place at the same time had a significant influence on me as a graduate student: Elias Dinas, who (alongside Dominik Hangartner) got a whole generation of political scientists in Europe excited about causal inference in the 2010s, and Eline de Rooij, who had worked with Don Green at Yale and had just written a review paper with Don and Alan on field experiments. I was interested in election campaigns and had encountered field experiments in my Master's, and Eline and I got talking about doing GOTV experiments with parties in Britain.

This resulted in the field experiments we did together in Birmingham in November 2012, I have been hooked on field experimentation ever since. What still fascinates me about the campaign experiments I do is how fast-paced it is and how closely you work with politicians and people involved in everyday politics. It also provides ample room for academic collaborations, which I enjoy. While doing the Birmingham experiments, I emailed Don, who invited me to come to Columbia for a few months. Don was incredibly inspirational as a teacher and took a lot of time for a PhD student who came to New York with ideas, enthusiasm, and no clue about what he was doing. Fast forward a couple of years to Zurich. I happened to sit in a seminar presentation by Nikolay Marinov, who presented a research project about how exposure to a Facebook campaign affects environmental attitudes. I must have left some impression on Nikolay during the seminar since he invited me to work with him and his co-authors on the project that day. That was the first social media-based experiment I got involved with (it's published in PSRM). The pandemic, which caused much of the in-person political campaigning to be disrupted and political and social life to shift online, did the rest.

What lessons can participants draw from your GESIS course?

Florian: First, I hope they will come away with ideas and enthusiasm for experimentation. Beyond that, they will hopefully learn that the design of social media experiments is full of non-trivial trade-offs they need to engage in at the design stage. They could also draw the lesson that effects on social media are usually small, but if they exist, they might scale, meaning we need to be powered to detect minor effects. And how do we do that? We’ll discuss statistical power and research designs that maximize power. I hope the course will also encourage them to look beyond Twitter to other platforms for experimentation. And we will design social media-based experiments together; that’s the essential part of the course.

What do you enjoy most about being a social scientist?

Florian: What I enjoy most is working with others and learning about the political and social world, in small steps of course, but still. There are so many amazing co-authors, inspirational practitioners, and talented students to work with – it never gets boring. It’s an incredible privilege to get to choose your collaborators and research projects. That’s quite unique when you compare it to any other job, say in industry, government, international organizations, or charities. I also enjoy getting the chance to work on research projects for multiple years and working on all aspects of research, from field work to data analysis and writing. While specialists in our fields, we are also generalists when it comes to the process of how we approach research projects.

We thank Florian for his interesting insights and look forward to his workshop.

Training Courses in English

09-12/05/23OnlineApplied Data Visualization
(Paul C. Bauer)
10-12/05/23MannheimIntroduction to Structural Equation Modeling for Cross Sectional Data
(Jochen Mayerl, Henrik Kenneth Andersen)
31/05-02/06/23OnlineAdvanced R Programming
(Tom Paskhalis)
06-07/06/23MannheimIntroduction to Geospatial Techniques for Social Scientists in R
(Stefan Jünger, Anne-Kathrin Stroppe)
19-20/06/23OnlineAutomated Reports & Co with Quarto and Markdown
(David Schoch, Chung-hong Chan)
26-28/06/23MannheimGeodata and Spatial Regression Analysis
(Tobias Rüttenauer)
29-30/06/23MannheimIntroduction to Quantile Regression
(Andreas Haupt, Sebastian E. Wenz)
05-07/07/23OnlineInteractive Data Analysis with Shiny
(Jonas Lieth, Paul C. Bauer)
05-06/07/23MannheimSocial Media-Based Field Experiments
(Florian Foos, Asli Unan)
17-19/07/23CologneLogistic Regression and Beyond: Modelling Categorical Dependent Variables
(Marita Jacob)
05-07/09/23OnlineIntroduction to R
(Natalia Umansky, Christian Pipal)
04-06/09/23OnlineIntroduction to Python
(Hannah Béchara, Paulina Garcia Corral)
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)
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)
13-15/12/23OnlineIntroduction to Stata
(Alexandra Asimov, Katrin Firl)

Training Courses in German

21-23/06/23MannheimGrundlagen und aktuelle Debatten der Regressionsanalyse
(Michael Gebel, Stefanie Heyne)
05-07/07/23MannheimEinführung in die Methoden der modernen Kausalanalyse
(Michael Gebel)
07-08/08/23MannheimQualitative Interviews
(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)
29/11-01/12/23CologneEinführung in die Strukturgleichungsmodellierung
(Marie-Ann Sengewald)
GESIS – Leibniz Institute for the Social Sciences, Department Knowledge Exchange & Outreach, GESIS Training, P.O. Box 12 21 55, 68072 Mannheim, training@gesis.org
Visit us at training.gesis.org
Copyright © 2023 GESIS. All rights reserved.

The GESIS data protection information can be viewed via the following link.