In general, I’m deeply interested in measuring political attitudes and opinions primarily using dimensional scaling and machine learning. So far, my research has worked to better understand COVID attitudes and behaviors and the dimensionality of affective polarization. In the future, I aim to continue studying affective and ideological polarization in the United States (and elsewhere), and their interrelationship.
See It in 3D: The Underlying Dimensions of Affective Polarization
Progressives and Never-Trumpers: Contrastive Principal Component Analysis as an Alternative Method for Public Opinion Research
Rational Voting in the Age of Ideological Polarization & Responsible Parties: Examining Presidential Elections from 1972–2020