GESIS Training

Don't forget to register for this course to master state-of-the-art computer vision methods at the ! ⏳

The course offers plenty of opportunities for hands-on exercises and consulting on your own project.

👉 https://t1p.de/ComputerVision

GESIS - Leibniz-Institut für Sozialwissenschaften

Do you want to use computer vision for analyzing image and video data? Join Andreu Casas in his course to gain theoretical & methodological expertise and benefit from hands-on tutorials & consulting sessions.

🎯 By the end of the course, you will:

✅ Have a good overview of the existing images-as-data literature in the social sciences.
✅ Have a good understanding of key deep learning concepts relevant for the implementation of computer vision methods.
✅ Have a good understanding of several computer vision techniques (object and face detection/recognition, image classification, facial trait analysis, etc.).
✅ Have a good understanding of the many options and techniques available to store and compute visual data.
✅ Be able to implement different computer vision techniques in Python.
✅ Be able to use/adapt different computer vision techniques for your own research projects.
✅ Be able to use/adapt different multimodal modeling approaches, including large visual language models.


More info & registration: https://t1p.de/ComputerVision



GESIS - Leibniz-Institut für Sozialwissenschaften

Don't scroll past this opportunity! 👇

Registration is still open: https://t1p.de/MobileDataCollectionAnalysis

👀 Check also the interview with Lukas & Julius on mobile collection methods at https://t1p.de/Training-NL-April_2026!

Learn from Lukas Otto & Julius Klingelhoefer how to study people's behaviors and experiences in real time with smartphone-based data collection and multilevel modelling at the .

🎯 Learning objectives
By the end of the course, you will:

✅ Have gained knowledge of the capabilities, benefits, and drawbacks of mobile intensive longitudinal designs.
✅ Be able to design and conduct mobile intensive longitudinal studies using the GESIS AppKit or other software.
✅ Understand the multilevel logic of intensive longitudinal data, e.g., within- and between-person effects.
✅ Be able to visualize, process, and clean intensive longitudinal data.
✅ Be able to analyze intensive longitudinal data using different basic and advanced methods depending on the data structure and goals of a given research project

More information & registration ➡️ https://t1p.de/MobileDataCollectionAnalysis


👀 Check the interview with Lukas & Julius on mobile collection methods at https://t1p.de/Training-NL-April_2026

GESIS - Leibniz-Institut für Sozialwissenschaften

Still curious about what happens when individuals interact in complex systems? 🧐

Don't miss Daniel Mayerhoffer's course on , where you'll learn how to simulate these interactions and explore their outcomes using and other programming languages.

👉 https://t1p.de/AgentBasedModeling

Curious about complex interactions between individuals? 🧐

Join Daniel r to learn how to simulate these interactions and their outcomes using and other languages in our course on .

🎯 Learning objectives
By the end of the course, you will:
✅ Be able to conceptualize common types of ABMs and implement them in NetLogo.
✅ Understand and apply all major NetLogo commands.
✅ Know the fundamentals of implementing ABMs independent of programming language.
✅ Use large language models as “collaborators” for defining and refining ABM concepts and implement these concepts in the programming language of your choice.
✅ Critically evaluate your own and others' ABMs in terms of internal and external validity.
✅ Identify potential use cases of ABMs in your research settings.
✅ Have a first version of your own ABM ready to be analyzed for a future research paper.

phases begin on Day 2, giving participants the opportunity to apply their learning in a collaborative, hands-on project throughout the remainder of the course.

Secure your spot at https://t1p.de/AgentBasedModeling



GESIS - Leibniz-Institut für Sozialwissenschaften

📣 Our June newsletter is out!

🤩 Registration for & is still open.
🔍 Interview with Camilla Salvatore on latent variable modeling for researchers.
✨ Updates on about causal inference methods.
📌 STD – 2027 – Metascience: Methods and Tools for Robust Inference

👀 ➡️ https://t1p.de/training-nl-June_2026

GESIS - Leibniz-Institut für Sozialwissenschaften

📣 New month, new chances!

Discover our latest :

📊 Advanced R Programming
🔄 Questionnaire Translation: Best Practices and LLM-Based Approaches
📈 Introduction to Longitudinal Structural Equation Modeling

Find yours ➡️ gesis.org/workshops 👇

*Advanced R Programming* by Tom Paskhalis.

🗓️ 21 – 23 October 2026
🏢 Online
🌐 https://t1p.de/r-programming-26

*Questionnaire Translation: Best Practices and LLM-Based Approaches* by Brita Dorer, Ulrike Efu Nkong & Dorothée Behr.

🗓️ 21 December 2026
🏢 Online
🌐 https://t1p.de/QuestionnaireTranslation

*Introduction to Longitudinal Structural Equation Modeling* by Daniel Seddig.

🗓️ 01 – 03 February 2027
🏢 Mannheim
🌐 https://t1p.de/Longitudinal_SEM

GESIS - Leibniz-Institut für Sozialwissenschaften

Wenn deine Daten hierarchisch strukturiert und Variablen latent sind, ist die Mehrebenen-Strukturgleichungsmodellierung ( ) die ideale methodische Wahl.

In unserem zweitägigen online lernst du, wie du MSM-Modelle spezifizieren, schätzen und interpretieren – und diese auf deine eigene Forschung anwenden kannst – praxisnah in R.

Zu den zentralen Themen gehören:

📌 Grundlagen der Strukturgleichungsmodellierung und Mehrebenenmodellierung
📌 Mehrebenen-Pfadmodelle mit Zufallseffekten im Achsenabschnitt (random intercept) und Steigung (random slope)
📌 Konfirmatorische Faktorenanalyse mit Mehrebenenstruktur
📌 Vollständige MSM-Modelle
📌 Fortgeschrittene Themen wie Messinvarianz und Zufallseffekte

Info & Registrierung ➡️ https://t1p.de/einf_mod_26

GESIS - Leibniz-Institut für Sozialwissenschaften

Planst du, latente Variablen mit kategorialen Daten zu analysieren? 📐💻

Dann nimm am zur Item-Response-Theorie ( ) teil und lerne, wie du IRT-Modelle zur Beantwortung substantieller und methodischer Forschungsfragen einsetzen kannst.

In praxisnahen Übungen mit R behandelst du unter anderem:

• Grundlagen latenter Messmodelle
• Datenaufbereitung für IRT-Analysen
• zentrale IRT-Modelle (Rasch, 2PL, 3PL, Partial Credit)
• verschiedene Schätzverfahren (JML, CML, MML)
• Fragen der Messqualität, z. B. Reliabilität, Validität und DIF
• Anwendungen von IRT-Modellen auf Quer- und Längsschnittdaten

Info & Registrierung ➡️ https://t1p.de/item-response-theorie-26

GESIS - Leibniz-Institut für Sozialwissenschaften

👥🗣️ Gute Gruppendiskussionen entstehen nicht zufällig – sie werden gezielt gestaltet!

Ob in der Forschung, Marktforschung oder Organisationsentwicklung – sind eine wertvolle Methode zur Analyse kollektiver Meinungsbildungsprozesse. Doch wie plant, moderiert und analysiert man sie wirkungsvoll?

Unser mit Thomas Kühn bietet einen praxisnahen Leitfaden von der Rekrutierung bis zur Datenanalyse.

Lerne, wie du:
✅ Gruppendiskussionen gezielt entlang deiner Forschungsfragen planst und strukturierst
✅ Teilnehmende rekrutierst und wirkungsvolle Gruppen zusammenstellst
✅ Diskussionen moderierst und Gruppendynamiken professionell steuerst
✅ Diskussionsdaten analysierst und Erkenntnisse sinnvoll in deine Forschung integrierst

Jetzt anmelden ➡️ https://t1p.de/gruppendiskussionen-26

GESIS - Leibniz-Institut für Sozialwissenschaften

Good causal analysis starts with the right graph!

Learn how to work with and tackle common pitfalls in causal inference at .

Register Now ➡️ t1p.de/GSS26-SCE

GESIS - Leibniz-Institut für Sozialwissenschaften

🚀 Boost your causal reasoning with Directed Acyclic Graphs ( )!

Our short course offers a solid introduction to the theory behind DAGs and their power for modeling causal relationships. Learn how to approach classic causal inference challenges — hands-on exercises in R.

Over three days, we cover:

✅ Day 1: Causal modeling, causal diagrams, d-separation.
✅ Day 2: Backdoor & front-door criteria, surrogate experiments, Z-identification
✅ Day 3: Causal discovery, covariate adjustment, and sensitivity analysis

Info & registration ➡️ https://t1p.de/GSS26-SCE

GESIS - Leibniz-Institut für Sozialwissenschaften

Call to bridge survey research and data science!

In our course, learn with Anna Steinberg Schulten and Fiona Draxler how to collect and analyze digital behavioral and traditional survey data using modern data science tools – and how to apply them effectively to real-world research questions.

The topics covered include:

• Data collection: Web scraping, APIs, and data donation
• Version control & collaboration: Working with Git and GitHub
• Machine learning: Regularized regression and random forests
• Text analysis: Topic modeling and analysis with LLMs
• Data quality: Challenges and error frameworks for digital data

Info & registration ➡️ t1p.de/GSS26-C4



You will find the full program of the Summer School and detailed course descriptions, and more information at t1p.de/GSS26-Program.

GESIS - Leibniz-Institut für Sozialwissenschaften