Affective polarization in a word: Open-ended and self-coded evaluations of partisan affect - Spencer Kiesel; Sharif Amlani (PLoS ONE, 2025)
The literature finds that partisanship drives negative emotional evaluations of out-partisans. Yet, scholars base these insights on measures–like thermometers, candidate evaluations, and social-distance measures–that discount the sentiment attached to individuals’ negative attitudes. We introduce a unique measure of affect capturing the motivation underpinning partisans’ attitudes. Our measure asks respondents for one-word to describe voters in their party and the opposing party. Then respondents code the sentiment behind their word choice themselves. Together, our measure produces qualitative and quantitative measures of respondents’ affect. We find that our self-coded open-ended measure has strong face validity and correlates strongly with existing affect measures. This measure advances our understating of partisan affect by allowing scholars a window into respondents’ state of mind. Scholars can easily apply our measure’s procedure beyond partisanship to other groups of interest.