Klara Müller, Chair of Political Science, Political Psychology: GLES Long- and Short-term Panel Studies (March 2024)

Klara is a doctoral candidate in Political Science at the CDSS, GESS. She is currently a research assistant at the Chair of Political Science, Political Psychology and project staff at the Mannheim Centre for European Social Research (GLES Long- and Short-term Panel Studies). She holds a Master's degree in Political Science from the University of Mannheim and a Bachelor's degree in Philosophy from the University of Tübingen. Alongside her studies, she worked as a research assistant at the Chair of Quantitative Methods in the Social Sciences at the University of Mannheim and at the Swiss Environmental Panel at ETH Zurich. For the 2021 German federal election, she was part of the election forecasting project zweitstimme.org and focused on constituency-level forecasts. In her current research, she analyses the influences of political events and political change on the willingness to participate in surveys and the associated measurement biases.

What is your current research topic?

In my research, I examine the extent to which political events and changes in the political context render some people less (or more) willing to participate in political surveys (e.g. election polls). I examine which groups of people are underrepresented in survey results after which types of political events. Based on this, I analyse whether these sample biases also lead to biased measurements of political phenomena.

For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?

I investigate when and why some people no longer want to participate in surveys about politics.

Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?

Interdisciplinary, versatile instrument

What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?

For both my dissertation and the project in which I am employed, I work almost exclusively with panel data (i.e. data in which the same people were surveyed at several points in time over a longer period). I analyse this data using quantitative methods and modelling. In addition, as part of my doctoral thesis, I employ simulation-based methods, so-called agent-based models. Methods for quantitative text analysis are also becoming increasingly established in the social sciences, and these are also increasingly playing a role in my research. All these methods fall under the term “Data Science”.

How high is the value of Data Science for your work? Would your research even be possible without Data Science?

The value is very high for my work. All areas of my research that are on a purely theoretical and conceptual level can of course also do without data science. But when it comes to empirically testing my questions and theoretical expectations, it would not be possible to conduct my research without data science.

What development opportunities do you see for the topic of Data Science in relation to your field?

I see great potential particularly around machine learning and the analysis of large, even unstructured data, such as texts, images, or videos. In recent years, these approaches have been used more and more frequently and yield many opportunities to investigate new questions or to shed light on established research questions employing new methods.