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GESIS Training
GESIS - Leibniz-Institute for the Social Sciences

GESIS Training News

March 2023

Spring Seminar | Summer School | Fall Seminar | Workshops

Table of Contents

GESIS Summer School in Survey Methodology 2023 – Registration is Open

The GESIS Summer School 2023 will take place from 02 to 25 August 2023. The Summer School will be organized as a hybrid event: Some courses will be held onsite in 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.

GESIS Workshops 2023 – Tailored to Your Needs

In the coming months, our schedule is packed with workshops for R newcomers and more experienced R enthusiasts: from an introduction to data wrangling and statistical programming to more advanced topics like functional programming, parallelization, and package writing as well as an introduction to data visualization, interactive data analysis with Shiny and fully-reproducible and professional automated reports with Quarto and Markdown – we've got you covered!

Moreover, we serve two approaches to text analysis: an introduction to the computational analysis of big textual data with R from a quantitative perspective, and grounded theory methodology, a core concept of qualitative analysis.

If you want to join others in collecting surveys online, look at our workshop on programing web surveys. Suppose you already have your data and want to analyze it. In that case, we offer introductions to structural equation modeling (SEM) and regression analysis. For advanced techniques, look at our workshops on spatial data, categorical dependent variables, and quantile regression. If you are interested in social relations, then network analysis is the way to go. We offer workshops on this topic from the egocentric- and qualitative perspective.

For further information and registration, please visit the workshop website.

GESIS Fall Seminar in Computational Social Science 2023 – Save the Date

The Fall Seminar takes place from 11 to 29 September 2023 at the GESIS premises in Mannheim. Its courses are taught by both GESIS and international experts and cover methods and techniques for collecting and analyzing digital behavioral data (“big data”), for example Machine Learning or Text Mining. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data.

The Fall Seminar targets social scientists, data scientists, and researchers in the digital humanities who collect and analyze data from the web, social media, or digital text archives.

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. All courses are held in English.

Registration will open in April and will be announced in our newsletter and on our social media channels!

For further information please visit our website.

Interview with Jessica Daikeler (GESIS) & Sonila Dardha (Meta)


Jessica Daikeler is a survey methodologist and works in the Survey Operations team in the Survey Design and Methodology department at GESIS. At GESIS, she is involved in the application of evidence-based methods, in particular experiments, systematic reviews, and meta-analyses. She has lots of experience with different systematic review and meta-analysis projects. Her research is currently focused on data quality in digital behavioral data linked to survey data and, of course, methods for the accumulation of evidence.


Sonila Dardha is a survey methodologist with an awarded PhD at City, University of London on the topic of interviewer effects. Currently, she is a Survey Methodologist Quantitative UX Researcher at Meta in London. Previously, she worked for Kantar Public in Brussels and London running international projects such as the Enterprise Surveys in Africa (World Bank), Eurobarometer Surveys (European Commission), European Elections 2015 (European Parliament), Life in Transition Survey (European Bank for Reconstruction and Development), and Global Attitudes Project (Pew Research Center).

They will teach the course “Applied Systematic Review and Meta-Analysis” at the GESIS Summer School in August 2023.

How did you become interested in your subject?

Jessica: For me, the answer to this question is very simple. My doctoral supervisor, Prof. Michael Bosnjak, gently but firmly pointed me to this topic. Ultimately the best thing that could have happened. It opened many doors for me to be an expert in a specialized field. Moreover, scientific journals like meta-analyses because they are often cited, so win-win.

Sonila: I started exploring systematic reviews and meta-analyses as methods during my PhD in sex-of-interviewer effects in survey research. I realized how important this approach is since it provides a more robust and comprehensive understanding of a research question and helps to identify trends in the results that might have gone unnoticed in individual studies. Given how powerful such studies can be - essentially moving from knowledge to wisdom, I dedicated more time to fully exploring this field. And here I am now, sharing that learning with others.

What lessons can participants draw from your GESIS course?

Jessica & Sonila: After this course, participants will be able to conduct an evidence gap map, a systematic review and/or meta-analysis. They will learn that although it is a lot of work, synthesizing studies can also be a lot of fun. Participants will be able to use the literature search techniques, including text mining methods, beyond systematic reviews in their academic careers.

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

Jessica: The most exciting development in our field is definitely the incredible data situation we are confronted with. In the future, the social sciences will not only have a large amount of survey data at their disposal, but also observational data (on digital behavior). I am very proud that GESIS is playing a leading role here and I am already very excited because this year our new web tracking panel "GESIS Pulse" and also the Survey App Kit for collecting such data will be launched. On the other hand, I still see many challenges, especially in the area of data protection.

What do you enjoy most about being a social scientist?

Sonila: I enjoy the opportunity to study human thinking and behavior, and to use my research to make a positive impact in our societies. I also find the process of collecting, analyzing, and interpreting survey data to be challenging and rewarding. The ability to identify patterns in how humans think, behave, feel, perceive, etc., and use that knowledge to inform decisions can be a source of satisfaction for me as a social scientist.

We thank Jessica and Sonila for their interesting insights and look forward to their class.

Training Courses in English

17-18/04/23OnlineUsing Smartphone Sensors, Apps, and Wearables in Social Science Research
(Florian Keusch, Bella Struminskaya)
17-18/04/23MannheimData Management, Advanced Programming and Automation using Stata
(Daniel Bela)
25-28/04/23OnlineIntroduction to Event History Analysis
(Jan Skopek)
03-05/05/23OnlineIntroduction to Computational Text Analysis with R
(Marco Wähner, Lena Masch)
08-10/05/23MannheimEgocentric Networks: Theory, Methods, and Applications
(Lydia Repke)
08-10/05/23OnlineIntroduction to R
(Judith Gilsbach)
09-12/05/23OnlineApplied Data Visualization
(Paul 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)
12/06/23OnlineLinking Twitter & Survey Data
(Luke Sloan, Libby Bishop, Johannes Breuer)
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
(Dennis Abel)
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)
28-30/11/23OnlineResearch Data Management and Open Science
(Anja Perry, Sebastian Netscher)

Training Courses in German

27-28/03/23OnlineEinführung in die Programmierung von Websurveys
(Jan Marquardt, Frauke Riebe)
(Günter Mey, Paul Sebastian Ruppel)
(Betina Hollstein, Laura Behrmann)
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)
29-31/08/23CologneQualitative 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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