GESIS Training News

The GESIS Training Team is very concerned about the health of participants and lecturers of our courses. However, we know that many of you (like us) look forward to meeting in person again. Thus we are happy that we started to offer in-person courses again in June. The GESIS Summer School will be carried out as a hybrid event. Please respect that local restrictions may apply due to the coronavirus. Because we know that some of you prefer online classes altogether, we have added digital formats to our regular program. More information here.

Stay well and safe!

Join us at the new GESIS premises in Mannheim from 05 September to 23 September and choose from various 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 GESIS and international experts and cover methods and techniques for working with digital behavioral data (“big data”). Week 1 comprises courses on the foundations of working with digital behavioral data, courses in Week 2 focus on the collection and management of big data, and courses in Week 3 cover different techniques for analyzing these data. 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 (05 - 09 September): Foundations of Working with Digital Behavioral Data

Introduction to Computational Social Science with R

Dr. Aleksandra Urman (University of Zurich), Dr. Max Pellert (Sony Computer Science Lab Rome)   

Introduction to Computational Social Science with Python

Prof. Dr. Milena Tsvetkova (London School of Economics), Dr. Patrick Gildersleve (London School of Economics)    

Tools for Efficient Workflows, Smooth Collaboration and Optimized Research Outputs

Dr. Julia Schulte-Cloos (University of Munich), Lukas Lehner (University of Oxford)   

Week 2 (12 - 16 September): Collection and Management of Digital Behavioral Data

Automated Web Data Collection with R

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

Automated Web Data Collection with Python

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

Big Data Management and Analytics

Prof. Dr. Rainer Gemulla (University of Mannheim), Adrian Kochsiek (University of Mannheim)   

Week 3 (19 - 23 September): Analyzing Digital Behavioral Data

Network Analysis in R

Dr. David Schoch (GESIS), Dr. Termeh Shafie (GESIS)    

Introduction to Machine Learning for Text Analysis with Python

Prof. Dr. Damian Trilling (University of Amsterdam), Prof. Dr. Anne Kroon (University of Amsterdam)    

Automated Image and Video Data Analysis with Python

Prof. Dr. Andreu Casas (Vrije Universiteit Amsterdam), Felicia Loecherbach (Vrije Universiteit 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 of the GESIS Fall Seminar can obtain 2 ECTS credit points per one-week course.

For those without any prior experience in R or Python and those who’d like a refresher, we’re additionally offering two pre-courses, “R 101” and “Python 101” (two days, online) in the week before the start of the Fall Seminar.

All courses 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 registering early.

Please visit our website for detailed course descriptions and registration and sign up here!

The GESIS Summer School 2022 will take place from 03 to 26 August 2022. The Summer School will be organized as a hybrid event: Some courses will be held on-site at an external venue in Cologne, and some online via Zoom.

Below you will find some selected courses from this year's program — each with a couple of places available:

Week 1 (08 - 12 August)

Introduction to Survey Design [on-site]

Prof. Dr. Bella Struminskaya (University of Utrecht), Prof. Dr. Peter Lugtig (University of Utrecht)   

(Non-)Probability Samples in the Social Sciences [online]

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

Week 2 (15 - 19 August)

Questionnaire Design [on-site]

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

Design and Implementation of Web Surveys [online]

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

Week 3 (22 - 26 August)

Applied Multiple Imputation [on-site]

Prof. Dr. Jan Paul Heisig (Free University Berlin and WZB), Dr. Ferdinand Geißler (Humboldt-University Berlin)   

Go to www.gesis.org/summerschool for the entire program, including detailed descriptions of this year’s courses on Directed Acyclic Graphs (DAGs), Data Science Techniques, Factorial Survey Design, Web Surveys, Mixed-Mode Surveys, Longitudinal Social Network Data, Questionnaire Design, Survey Design, Stata or R, Sampling and Weighting, (Non-)Probability Samples, and Multiple Imputation.

ECTS Credits

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 (24hrs or 30hrs). More information is available here.

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

The GESIS Spring Seminar offers high-quality training in state-of-the-art techniques in quantitative data analysis taught by leading experts in the field. In 2023, all courses will deal with "Modeling Group Differences" in the social sciences and beyond. It targets advanced graduate or PhD students, post-docs, and junior and senior researchers. Lectures in each course are complemented by extensive hands-on exercises and tutorials. All courses are held in English. The Spring Seminar will take place from 27 February to 17 March 2023 — either at the GESIS premises in Mannheim or Cologne.

Registration will be open in October and then announced in our newsletter! On our website, you will then find detailed course descriptions and will be able to register for the courses.

Compared to GESIS's other formats, the workshops are one- to three-day courses throughout the year. We provide workshops on all levels of expertise, starting with introductory up to advanced level courses on data analysis, computational social science, and survey methodology, offering a broad range of topics and methodological approaches for empirical social scientists. Our workshops are offered in German and English and take place online and at both GESIS locations in Mannheim and Cologne. In the second half of 2022, we are looking forward to introductory courses for working with R, Python, and Stata, and courses on data analysis, such as multilevel, panel data, sequence analysis, structural equation model, and a course on research data management. Below is a list of all confirmed workshops for the second half of 2022. However, we are continuously adding new courses to the workshop program. Thus, it is worth visiting our website and following us on Twitter for updates.

More information on the GESIS workshops you will find here.

Bella is an Assistant Professor of Methods and Statistics at Utrecht University and an Affiliated Researcher at Statistics Netherlands. Her research focuses on the design and implementation of online, mixed-mode, and smartphone surveys, and passive data collection. She has published on data quality, nonresponse and measurement error, panel conditioning, device effects, and smartphone sensor measurement. With Florian Keusch, she will teach the course “Using smartphone sensors, apps, and wearables” within the GESIS Workshop program in October. Florian is a Professor of Social Data Science and Methodology at the University of Mannheim and an Adjunct Assistant Professor at the University of Maryland. His research concerns modern methods of collecting data for the behavioral and social sciences, particularly mobile web surveys and passive data collection through smartphones, wearables, or digital traces.

How did you become interested in your subject?

Bella: The interplay between technological and societal developments has always fascinated me. In my PhD, I focused on data quality in online surveys and later on errors in surveys conducted on mobile devices. My current research agenda is the continuation of this theme and explores “designed big data”, that is, combining passive smartphone data collection with self-reports through surveys in ways that evaluate and aim to decrease nonparticipation bias and measurement error while assuring the efficiency of data collection. I am also focusing on issues of selectivity and consent in data donation studies, where participants are asked to share their digital trace data with researchers. Questions such as who donates their Instagram data, Google location history data, or smart meters data, can these data be used to answer substantial research questions, how can we optimally combine digital traces and self-report are central but also how to design the donation process such that research participants have control over the data they share. The demand for high-quality data is high from academic researchers, national statistical institutes, and clients such as ministries, for example, that is reflected in the affiliations of participants of our courses. Ultimately, we wish to help social science researchers answer research questions that previously were difficult or impossible to tackle, but the novel data collection methods allow it. For example, a sociologist from the USA, Naomi Sugie, has equipped men recently released from prison with smartphones to study how they re-integrate into the labor market and socially. Without the objective data about the participants’ geographical movements that were linked to the job offering data, researchers would not have seen that there exists a mismatch between available positions and places where participants dwell. Such research can have important policy implications. Courses such as ours, that focus on the implementation of social science studies using mobile devices, apps, sensors, and wearables in ways that maximize data quality can have implications for research but also for societies more broadly.

Florian: Like Bella, I have a background in survey methodology. I also originally studied (and still study) measurement and representation error in web and mobile web surveys. Adding smartphones as a data collection tool was a natural next step in the evolution of survey data collection because many people now own these devices and carry them around throughout the day. This allows us to not only ask study participants in-the-moment survey questions but also to passively collect information about their behavior via the sensors built into the devices. This reduces the burden of data collection for the participants and provides us with more detailed and more accurate data. Smartphones are, of course, not the only devices that have sensors built into them, so going one step further and using fitness trackers, smart watches, and other smart devices for data collection is becoming increasingly popular in the social, behavioral, and health sciences. My interest here is mainly in understanding the quality of these new types of data. How does data from a sensor about physical activity differ from self-reported physical activity? Who do we miss if we ask people to share their sensor data with us, and what are the concerns people have if they are not willing to share their data? I am also interested in understanding under what circumstances it is better to ask a survey question than to rely on sensor data, and vice versa and when do we need information from both self-reports and sensors to fully understand a phenomenon. My methodological research is inspired and driven by substantive research questions, for example, on the effects of unemployment on social interaction and integration efforts of refugees.

What lessons can participants draw from your GESIS course?

Bella+Florian: This course mainly focuses on designing studies involving smartphones and wearables (e.g., smartwatches, fitness bracelets) that allow the collection of data via sensors (e.g., GPS, accelerometer) and apps. This passive mobile data collection, for example, of location, physical activity, and call or browsing history, can augment or replace self-reports in surveys. However, there are multiple challenges to this approach, including participant selectivity, (non)willingness to provide sensor data, privacy concerns and ethical issues, quality and usefulness of the data, and practical issues of implementation. In this course, we discuss the challenges researchers face when conducting studies using wearables, apps, and sensors by reviewing state-of-the-art practices from the literature and our own research, and we provide best practices and practical recommendations. We encourage course participants to discuss their own study designs.

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

Bella+Florian: The field is constantly evolving as technology moves forward, which is exciting! While we mostly focus on wearables, apps, and sensors in our course, we could talk about swallowables – tiny sensors that people can ingest. These are mostly used in medical research, but the line between social sciences and health / medical research is thinning – just think about collecting biomarkers in surveys as is done by some large-scales surveys on aging. One could debate (and we hope to do so with our course participants!) about the necessity of self-report in light of recent developments around passive sensing and data donation. There are privacy issues, informed consent, and combining different data sources (sensors, self-report, administrative and other auxiliary data) – too many to name here. We are currently writing a book about data collection using wearables, apps, and sensors: be on the lookout for the first chapters to appear online to learn more about these exciting topics!

We thank Bella and Florian for their interesting insights and look forward to their class.