GESIS Training News

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!

We are proud to present the newest member of our GESIS Training family, the GESIS Fall Seminar in Computational Social Science. Successor of the GESIS Methods Seminar, the Fall Seminar focuses exclusively on Computational Social Science methods. Organized along two parallel tracks, it offers three one-week courses each that will allow participants to go from zero to hero in Computational Social Science methods using either R or Python. In 2021, the GESIS Fall Seminar takes place online from 13 September – 1 October.

Stay tuned for the detailed course program, which we will announce in our next newsletter. You can also follow any updates on our website!

Carina is a postdoctoral researcher at the Collaborative Research Center SFB 884 “Political Economy of Reforms”, University of Mannheim. Together with Prof. Annelies Blom, she also leads the project “Recruiting Probability-Based Online Panels: Cost-Efficiency and Data Quality” at the Mannheim Center for European Social Research. In addition, she is an associate editor of the Journal of Survey Statistics and Methodology. She studied Sociology and Political Science in Mainz and Frankfurt and received her doctoral degree from the Center for Doctoral Studies in the Social Sciences at the Graduate School of Economic and Social Sciences, University of Mannheim. Her teaching experience at the University of Mannheim School of Social Sciences includes courses on statistical data analysis (B.A.-level) as well as on data and measurement (M.A.-level). Her research focuses on innovation in the quantitative social science research methods, in particular on recruiting and maintaining online panels, probability-based and nonprobability survey samples, mixed-mode and mixed-device data collection, and enhancing survey data with other types of data. She will teach the course on “(Non-)Probability Samples in the Social Sciences” within the frame of the GESIS Summer School in August 2021.

How did you become interested in your subject?

When I started grad school, one of my teachers told me that he always recommends his students to select the cheapest, most readily available sample for their research, because the statistical analysis can remove all errors and biases in the data. Because I had previously been socialized into a research environment where following a textbook approach to sampling and data collection was the norm, I protested against this viewpoint. I argued that you should always design the best possible research study to collect data that are (as much as possible) free of errors and biases and all other data are essentially worthless. I have since come to realize that neither of us was completely right and neither of us was completely wrong. Instead, we need to decide on a case-by-case basis whether a given sample can meaningfully contribute to answering a research question or not. This decision needs to be based on statistical theory and on empirical evidence. To help researchers make informed decisions about their samples, I contribute to gathering empirical evidence, deriving best-practice recommendations, and discussing this important topic within the scientific community.

What lessons can participants draw from your GESIS course?

Participants can draw from my course an understanding of the different positions in the debate about probability and nonprobability samples, which was not only taking place in my grad school, but is actually an ongoing “hot topic” discussion in quantitative social sciences. They will also learn about the accumulated empirical evidence and best-practices so far as well as possibilities for combining the advantages of different types of samples in survey practice. The overall goal is to empower participants to think critically about the research studies that are currently being conducted and discussed in the social sciences and feel comfortable with deciding about the pros and cons of different samples for their own research.

What do you enjoy most about being a social scientist?

I particularly enjoy the wide variety of important research topics, methods, and theories, that enable us to have so many exciting debates, including the one I will focus on in my GESIS Summer School course.

We thank Carina for her interesting insights.