Research

Google Scholar provides an overview of my publications (you can sort by year). Reproduction/replication material can be found on the Harvard dataverse (you can search for the title of study).

I work at the intersection of social data science, methodology, artificial intelligence, digital behavioral data and (political) sociology. I have a strong interest in survey methodology (e.g., Bauer et al. 2017), causal inference (e.g., Bauer 2015), experiments (e.g., Bauer & Clemm von Hohenberg 2021) and machine learning (e.g., Landesvatter & Bauer 2025). And, I worked extensively on the concepts of social (e.g., Bauer & Freitag 2018, Bauer 2021) and political trust (e.g., Bauer & Fatke 2014). Below some recent publications and current working papers.

Recent publications:

• Comparing speech-to-text algorithms for transcribing voice data from surveys, Public Opinion Quarterly (2026)
• How valid are trust survey measures? New insights from open-ended probing data and supervised machine learning, Sociological Methods & Research (2025)
• Can we predict multi-party elections with Google Trends data? Evidence across elections, data windows, and model classes, Journal of Big Data (2024)

Current (public) working papers:

• Information content in text and voice response formats for open-ended survey questions (accepted at JSSAM)
• Do emotions affect political trust judgments? (accepted at EJPR)
• From ideal experiments to ideal research designs (IDRs): What they are and why we should use them more (submitted)
• How should we visualize data? Lessons learned from reviewing high quality graphs in the Socius Special Collection Data Visualization