Each week, Daniels is featuring a researcher who conducts meaningful research that impacts their field and the wider community. Learn more about their work in Q&As with the Daniels Research team and email them to nominate yourself or a colleague for a future Q&A.
Stefani Langehennig, PhD, is an assistant professor of the practice in the Business Information and Analytics department at the University of Denver’s Daniels College of Business. She also co-directs the Center for Analytics and Innovation with Data (CAID) and helps lead the MSBA Capstone Program at DU. She teaches courses on Python programming, data mining and visualization, big data management platforms, frequentist statistics and project management. With a political science background, Stefani completed a postdoctoral fellowship at Birkbeck College, University of London and worked as a lead data scientist in consulting firms in London and the U.S., helping organizations build analytics teams, evaluate and implement data analytics solutions. Stefani has published academic articles on topics such as the impact of data transparency on political behavior, legislative policy capacity, the diffusion of environmental policy and the influence of political indicators on state-level lawmaker behavior.
What do you study, and how did you become interested in this area of research?
My PhD is in political science, and I also focused heavily on statistics. During graduate school, I studied American political institutions and the policy-making process, particularly how data and methodology influence lawmaking. Later, I worked in public policy consulting in the UK and EU, building data tools to understand how policy was being created, implemented and evaluated. Today, my research centers on institutional performance, evidence-based policymaking, data transparency and democratic accountability.
Can you share an example of a research project you’re especially excited about?
I’ve been collaborating with a colleague from my postdoc on a project examining transparency in the UK government. We received a grant from the Leverhulme Trust that allowed us to survey UK politicians and gather extensive data on voting records. What we found was fascinating: while citizens and interest groups crave transparency, lawmakers often feel “watched” when the public has access to data monitoring tools and they react by becoming less transparent. This tension between democratic accountability and lawmakers’ own behavior has been a particularly exciting area of study.
What kinds of data and methods do you use in your work?
I use a mix of qualitative and quantitative approaches. For the UK transparency project, we surveyed politicians, scraped data from platforms like TheyWorkForYou (TWFY), tracked political communication on Twitter (now X), carried out experimental “living labs” and conducted case studies of specific events. Beyond that, much of my research involves large-scale data collection, advanced statistical modeling and building tools to analyze policy processes and lawmaker behavior
How do you bring your research into the classroom?
In teaching, I emphasize hands-on skills and the idea of approximation—understanding that no model perfectly captures reality, but good methods help us get close. I share my own projects, datasets and even GitHub repositories so students see what data analysis looks like in practice. I also encourage students to bring in datasets connected to their own work or interests, so the techniques they learn are directly relevant to their future careers.
How has your research influenced policy or society more broadly?
My work has been submitted to UK parliamentary committees and informed discussions with the U.S. Senate Appropriations Committee about legislative productivity and funding practices. Recently, I’ve been focusing on AI policy, analyzing how states are approaching AI legislation. I am a finalist for a grant from the American Political Science Association (APSA) to launch an AI policy tracker through (CAID), which will serve as a dashboard to monitor state-level AI policymaking over time. This work connects directly to pressing questions about governance and technology, and also helps inform our substantive understanding of how states are approaching AI policy
What’s next for your research?
I’m developing the AI policy tracker project and looking forward to engaging students in that process. By combining political science, data science and real-world policy impact, I hope to build tools that not only advance scholarship but also inform how lawmakers, organizations, and the public navigate complex issues like AI and democratic accountability.