About Me

About

I am a quantitative methodologist interested in how we can reliably measure social and behavioral phenomena. Being convinced that meaningful empirical research should be as rigorous and trustworthy as the public expects it to be, my aim is to play a part in fostering an open, reproducible, and methodologically robust research culture in the social sciences. I want to help strengthening the credibility of empirical findings by drawing from my knowledge of statistics and survey methodology to provide high-quality data and robust workflows for researchers.
My own research focuses on survey statistics and data quality, with a current focus on improving longitudinal labor market panel data. I currently pursue a PhD in Social Data Science & Research Methodology at the Institute for Employment Research (IAB) and the University of Mannheim. I hold a Research Master’s in Methodology and Statistics, with a specialization in official statistics, including the European Master of Official Statistics, as well as a Bachelor’s degree in Psychology from the University of Witten/Herdecke.

What got me into Methods ands Statistics?

I became drawn to methods and statistics when I first encountered the so called “replication crisis” in psychology and the broader social sciences. It was quite frustrating to realize that many influential findings were difficult to reproduce. Beginning to understand how much empirical research rests on the foundations of measurement quality and methodological choices, the open science movement provided a response that resonated with me: clearer documentation, transparency, more rigorous analytical approaches and working on establishing best practices in empirical research. This shift in the field shaped my own path, motivating me to focus on data quality, workflows in academia, and the statistical principles that make research more credible and trustworthy.

Research interests

• Measurement Quality

My foundational interest lies in how social and behavioral phenomena are translated by data. Measurement quality is central to this process as the validity, reliability, and comparability of indicators determine the credibility of findings. Poor measurement cannot be rescued even by the most sophisticated statistical models, while careful operationalization and validation of measurement instruments, while slow and tedious, enables clearer inference and better theory-building.

• Longitudinal Data

My interest in measurement naturally extends to longitudinal data, where issues of consistency and comparability become even more critical. If we want to understand changes over time, we need to be able to distinguish between changes caused by developing behaviors or attitudes and inaccuracies in our measurements. Longitudinal designs highlight the dependence of temporal inferences on measurement decisions, reinforcing the idea that rigorous operationalization and data quality are prerequisites for studying social processes.

• Metascience

The replication crisis has made clear that many weaknesses in empirical research arise not only from (rash and unquestioned) statistical practices, but from fundamental issues of measurement - something that hasn’t gotten nearly enough attention. Metascience provides a lens to examine how research is conducted, how evidence accumulates (and what hinders it), and how institutional incentives shape methodological choices.

• Philosophy of Statistics

Engaging with metascientific debates has led me toward deeper questions in the philosophy of statistics, which provides the conceptual tools for linking data to claims about the world. Issues of uncertainty, model adequacy, evidence, and inference are not purely technical - they are philosophical questions about how we reason and obtain new knowledge with data. Statistics is my chosen approach to tackling fundamental issues in the philosophy of science about the broader logic of empirical inquiry. Specifically, the social sciences deal with complex, content-dependent, and often reflexive constructs (that’s a tough one), which makes the challenges of measurement, explanation and causal inference uniquely demanding.