Daniel J. Hopkins

Daniel J. Hopkins studies how citizens form attitudes toward immigration and how local political context shapes those attitudes. His most-cited indexed work, politicized places theory, explains when and where immigrant arrivals provoke local opposition, while a conjoint analysis of attitudes toward immigrants revealed a hidden American consensus beneath apparently polarized views. Related work examines whether correcting misinformation about immigration changes attitudes, finding notably muted effects. Across these studies, the through-line is a methodological commitment to measuring attitudes precisely, including anchoring vignettes designed to correct interpersonal incomparability in surveys. Beyond these, Hopkins contributed as a middle author to causal inference in conjoint analysis, a widely cited framework for understanding multidimensional stated-preference experiments, and to political ideology prediction from Twitter users, bridging computational text analysis with political behavior research. His earlier