GESIS Training News

The 38th Methods Seminar took place July 9-27, 2018 at GESIS Cologne. All courses -- the well established as well as the two new courses on data science taught in English were well received, as the results of the course evaluations show. Particularly well received was the close interaction between theory and practice. Participants in particular appreciated that the lecturers were "didactically top, scientifically top and personally top" and provided "very well prepared material, especially [...] well commented syntax files and slides". The provided "mixture of the necessary mathematical derivation of procedures with the application-oriented reference" made even complex topics accessible to the participants. [Continue reading on facebook…]

A successful tradition has been continued. Once again GESIS hosted the 7th Summer School in Survey Methodology from August 2-24, 2018. More than 200 participants from the international academic community made Cologne their home and took part in a series of excellent courses on methods and techniques of survey methodology. They enjoyed inspiring discussions and got to know each other during Europe’s leading summer school in survey methodology. [Continue reading on facebook…]

If you could not make it this summer or want to come back, please save the date for the 8th GESIS Summer School in Survey Methodology that will take place August 1-23, 2019.

We are excited to announce the program of the 48th GESIS Spring Seminar 2019. The GESIS Spring Seminar comprises three training courses for social scientists interested in advanced methods of quantitative data analysis. Lectures in each course are complemented by hands-on exercises giving participants the opportunity to apply these methods to data. For 2019, the GESIS Spring Seminar will focus on Bayesian Modelling in the Social Sciences.

Week 1 (March 11 - 15): Introduction to Bayesian Models for the Social Sciences

Prof. Dr. Susumu Shikano, University of Konstanz, Germany

Dr. Taehee Kim, University of Oldenburg, Germany

Week 2 (March 18 - 22): Bayesian Structural Equation Modelling

Prof. Dr. Rens van de Schoot, Utrecht University, Netherlands

Dr. Milica Miočević, Utrecht University, Netherlands

Week 3 (March 25 - 29): Bayesian Multilevel Modelling

Dr. Mark Andrews, Nottingham Trent University, United Kingdom

The courses 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. We also recommend you to book your accommodation as soon as possible as rooms in Cologne can be scarce. Thanks to our cooperation with the Cologne Graduate School in Management, Economics and Social Sciences at the University of Cologne, doctoral students can obtain 3 ECTS credit points per one-week course.

For registration, please visit our website and sign up here!

EUROLAB is pleased to invite applications for research visits at its research lab in Cologne. Individual researchers who want to work on data available at GESIS may apply for support to access EUROLAB for a period of one month from April to June 2019. During their stay, visiting researchers will profit from a wide range of services including an individual workspace, access to data and to GESIS library with various online databases, as well as direct contact to GESIS experts.

The call opens at the end of October! Further details on the eligibility criteria and application procedure can be found here!

Deadline for applications is November 30, 2018.

Daniel will conduct the course “Data Management, Advanced Programming and Automation using Stata” within the frame of our workshop series in January 2019.

How did you become interested in your subject?

During my social science studies I developed a strong interest in programming and data preparation topics. In assistance jobs at research institutions, I quickly learned that there is a uniform understanding of how data analysis should be carried out. However, there seemed to be no common understanding of how to generate an analysis dataset leading to these analyses in the first place. As a result, I found that many researchers have different approaches for creating a ready-to-use analysis dataset. Through my inclination to computer science, parallels to software development became clear to me. As a result, I started thinking data preparation as a process of software development, and using a programmer's mindset when implementing data preparation and analysis in research projects.

What lessons can participants draw from your GESIS course?

The primary aim of this course is to raise awareness of the issue that reproducability of research results goes hand in hand with well programmed data preparation and analysis procedures. Also, such procedures increase the code's maintainability and its potential for reuse. Once researchers have become accustomed to a true programming workflow, they will gain a lot of potential for improving efficiency and saving time in future projects. The course program is designed to provide participants with the required skills and the toolset to develop a programming workflow for their own projects, using the statistical software package Stata. Attendees will be enabled to create reusable program routines, which will not only save them time in the future. It is also the first step for sharing code with other Stata users.

What do you think is the most exciting recent trend in Social Sciences?

Over the last decade, I think one of the most interesting trends in research projects of our field is the trend towards secondary data analysis. One implication of this trend is that the ability to cope with data structures that have been produced by someone else become more and more vital for young researchers. Doing this in a reproducible way is, in my opinion, a key skill in modern resarch projects in Social Sciences.

We thank Daniel Bela for his interesting insights.