My PhD deals with the replicability and robustness of observational social science research and is part of a larger DFG‑funded priority program on replicability in the behavioral, social, and cognitive sciences (META-REP). I am passionate about open science and strive to improve the current state of research by engaging in meta-scientific questions.
In a large-scale field experiment, my co-authors and I gathered research code (R scripts, do-files, etc.) from 385 researchers across the social sciences. These materials constitute a unique dataset to assess the reproducibility, replicability, and robustness of published observational social science literature. Unlike previous large-scale replication studies, we focus on non-experimental research. In doing so, we acknowledge that researcher degrees of freedom in secondary data analysis unfold downstream of data collection (e.g. outlier management, weighting, selection of control variables, etc.), which adds model uncertainty to sampling uncertainty. To deal with this, our project develops and expands computational tools which make social science more robust and reliable (e.g. multiverse analysis).
Other projects I have been involved in concern the visualization of multifactorial research designs and the state of open science policies at academic journals. In my teaching, I make an effort to pass on my passion for open science, experimental research designs, and data visualization to students. As a non-academic side project, I have been planning a training on statistical literacy for high school students.
If you want to get in touch with me, feel free to send me an e-mail. I am looking forward to hearing from you!