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GESIS - Leibniz-Institute for the Social Sciences

GESIS Training

GESIS Training News

April 2021

50th GESIS Spring Seminar 2021

This year’s Spring Seminar focused on the topic “Causal Inference” and covered three courses on “Causal Inference and Experiments” (Dr. D.J. Flynn, IE University Madrid), “Causal Inference in Observational Studies” (Dr. Krisztián Pósch, University College London, and Thiago R. Oliveira, London School of Economics), and “Causal Machine Learning” (Dr. Michael Knaus, University of St. Gallen, and Gabriel Okasa, University of St. Gallen). Participants rated all three courses as highly enjoyable, with 85% reporting to have been (very) satisfied with their course. Participants particularly emphasized the scientific and didactical competence of the lecturers, the practical exercises, and the high quality, up-to-date learning materials. They also greatly appreciated the “great atmosphere” and getting “new insights into the latest research methods.” To provide some additional room for networking and exchange, participants of the Spring Seminar were invited to present and discuss their ongoing research in a Flash Conference on March 2. [Continue reading on facebook…]

We are already preparing the theme for next year and will keep you updated through our website, newsletter, and social media.

Restrictions in our program due to the Coronavirus

We care a lot about the health of our participants and lecturers. For this reason, all our events will be conducted as online training courses until the end of December 2021. This also applies to the GESIS Summer School in Survey Methodology and the GESIS Fall Seminar in Computational Social Science. As things stand at present, we are planning to continue our events program after this date with on-site courses. Should this not be possible, we will continue to offer digital formats as an alternative. More information here .

Take care of yourselves!

GESIS Fall Seminar in Computational Social Science – Registration is open!

We are excited to announce the program of the GESIS Fall Seminar in Computational Social Science 2021, held virtually from 13 September – 01 October 2021. The GESIS Fall Seminar targets social scientists, data scientists, and researchers in the digital humanities who want to collect and analyze data from the web, social media, or digital text archives. Organized along two parallel tracks, it offers six one-week courses on computational social science methods and techniques using either R or Python. 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:

Computational Social Science with R

Introduction to Computational Social Science with Applications in R (13 - 17 September)

Dr. Aleksandra Urman (University of Bern / University of Zurich, Switzerland), Max Pellert (Medical University of Vienna / Technical University of Graz, Austria)

Automated Web Data Collection with R (20 - 24 September)

Dr. Theresa Gessler, Hauke Licht (University of Zurich, Switzerland)

Social Network Analysis with R (27 September - 01 October)

Dr. Silvia Fierăscu, Ianis Rușitoru (West University of Timișoara, Romania)   

Computational Social Science with Python

Introduction to Computational Social Science with Python (13 - 17 September)

Dr. Orsolya Vásárhelyi (University of Warwick, United Kingdom), Luis Natera (Central European University Budapest, Hungary)

Web Data Collection and Natural Language Processing in Python (20 - 24 September)

Indira Sen, Dr. Arnim Bleier, Julian Kohne, Dr. Fabian Flöck (GESIS, Germany)

A Practical Introduction to Machine Learning in Python (27 September - 01 October)

Assoc. Prof. Damian Trilling, Assist. Prof. Anne Kroon (University of Amsterdam, The Netherlands)            

Courses will be held online via Zoom and can be booked either separately or as a block. 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 of the Fall Seminar may earn 2 European Credit Transfer System (ECTS) points per course for active participation.

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

10th GESIS Summer School in Survey Methodology

In 2021 we celebrate the 10th birthday of the GESIS Summer School. It will take place from 28 July – 20 August 2021. In its anniversary year, it will be held again online as a virtual summer school for the second time since it was established in 2012. 14 courses are scheduled, among them 4 short courses and 10 one-week courses. Seven courses are new or have been completely redesigned: “Using Directed Acyclic Graphs for Causal & Statistical Inference”, “(Non-) Probability Samples in the Social Sciences”, “A (Short) Course on (Short) Scale Development”, “Mixed-Methods and Multimethod Research”, “Collecting and Analyzing Longitudinal Social Network Data”, “Pretesting Survey Questions”, and “Design and Implementation of Web Surveys”.

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

Short Courses (28 - 30 July):

Introduction to Stata for Data Management and Analysis

Nils Jungmann, Anne-Kathrin Stroppe (GESIS, Germany)

Using Directed Acyclic Graphs for Causal & Statistical Inference

Julian Schuessler (Aarhus University, Denmark)

Pretesting Survey Questions

Dr. Cornelia Neuert, Dr. Timo Lenzner (GESIS, Germany)   

Week 1 (02 - 06 August):

Introduction to Survey Design

Prof. Dr. Bella Struminskaya (University of Utrecht, The Netherlands), Dr. Ulrich Krieger (University of Mannheim, Germany)

Questionnaire Design

Prof. Dr. Marek Fuchs (Darmstadt University of Technology, Germany)

Introduction to R for Data Analysis

Dr. Johannes Breuer, Dr. Stefan Jünger (GESIS, Germany)

Survey Sampling and Weighting

Dr. Simon Kühne (Bielefeld University, Germany)            

Week 2 (09 - 13 August):

Statistical Analysis of Incomplete Data

Dr. Florian Meinfelder, Angelina Hammon (University of Bamberg, Germany)

Design and Implementation of Web Surveys

Prof. Dr. Christopher Antoun (University of Maryland, United States), Prof. Dr. Frederick Conrad (University of Michigan, United States), Prof. Dr. Florian Keusch (University of Mannheim, Germany and University of Maryland, United States)

(Non-)Probability Samples in the Social Sciences

Dr. Carina Cornesse (University of Mannheim, Germany)

Designing, Implementing, and Analyzing Longitudinal Surveys

Dr. Tarek Al Baghal (University of Essex, United Kingdom), Dr. Alexandru Cernat (University of Manchester, United Kingdom)

Week 3 (16 - 20 August):

A (Short) Course on (Short) Scale Development

Dr. Clemens Lechner (GESIS, Germany)

Mixed Methods and Multimethod Research

Prof. Dr. Ingo Rohlfing (University of Cologne, Germany)

Collecting and Analyzing Longitudinal Social Network Data

Dr. Lars Leszczensky, Dr. Sebastian Pink (University of Mannheim, Germany)

Scholarships, ECTS Credits, & More

Scholarships (fee waivers) are available to Summer School participants sponsored by the German Academic Exchange Service (DAAD) . Application deadline is 25 April 2021 – apply now!

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.

Register now! You will find the full program, detailed course descriptions, and more information here .

Interview with Dr. Orsolya Vásárhelyi (University of Warwick) & Luis Natera (Central European University Budapest)

Orsi Orsolya is a postdoctoral fellow at the University of Warwick, Center for Interdisciplinary Research. Her research focuses on the gender differences in career development in project-based environments. She has been teaching Python for social scientists for 4 years. Together with Luis Luis she will teach the course on “ Introduction to Computational Social Science with Python ” at the GESIS Fall Seminar in September 2021. Luis is a Ph.D. candidate of network science at the Department of Network and Data Science at the Central European University, in Budapest, Hungary. His research revolves around urban mobility in multiplex urban networks, bridging urban planning and complex systems. He has experience in interdisciplinary research and has worked in academic, government and private sectors.

How did you become interested in your subject?

O : I started to code in Python 5-6 years ago to be able to work with large-scale data more efficiently ( than in R :)).

L : I became interested in Python around 5 years ago when I wanted to analyze social media data in an efficient way. Later I started applying Python to model and analyze cities and urban mobility, with a background in architecture I found in coding (and specially Python) a very flexible tool to analyze large data sets. It enabled new possibilities for complex analysis.

What lessons can participants draw from your GESIS course?

O : After taking the intro to CSS with Python course, participants will be able to collect, manage and analyze data on their own. We emphasize self-learning and ethics a lot throughout the course, so participants can keep learning Python afterward and make conscious decisions about ethical data use and research.

L : I agree with Orsi's response, I would emphasize that the data collection and analysis will be from "real world" API's and webpages, providing them with a first hand experience, rather than working with pre-cleaned datasets.

What do you enjoy most about being a social scientist?

O : I love being a computational social scientist because it is an extremely interdisciplinary field, which connects so many incredible people with very mixed backgrounds. Innovation is an everyday practice in this field.

L : For me what I enjoy most of working in this field is getting to know different interdisciplinar approaches, and people working in creative ways to solve hard problems. Especially combining a background in social sciences with computational methods.

We thank Orsi and Luis for their interesting insights and look forward to their class.

Training Courses in German, May – December 2021



Ereignisdatenanalyse: Einführung und fortgeschrittene Anwendungen



Einführung in die Analyse von Strukturgleichungsmodellen für Querschnittsdaten



Mixed Methods: Angewandte Integration qualitativer und quantitativer Methoden in den Sozialwissenschaften



Mehrebenenanalyse mit Stata



Einführung in die Qualitative Inhaltsanalyse



Einführung in die Logik bayesscher Statistik für die Sozialwissenschaften



Einführung in die Methoden der modernen Kausalanalyse

Training Courses in English, April – December 2021



Data Management, Advanced Programming and Automation using Stata



Applied Data Visualization



Linking Twitter & Survey Data



Questionnaires for Cross-Cultural Surveys



Causal Mediation Analysis



Sequence Analysis in the Social Sciences



Tools and workflows for reproducible research in the quantitative Social Sciences



Sampling, Weighting and Estimation



Digital Trace Data in Social Science


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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