The American National Election Study regularly asks respondents to place themselves on an "Extremely Liberal" to "Extremely Conservative" scale (LibCon). Responses to this routinely included question increasingly contain valid and useful information for a wide range of political analyses.
This paper explores partisanship as a social identity, examining its impact on political polarization. Using mixed-methods and survey data from the American National Election Studies (ANES), it analyzes how partisan identity influences individuals' views of opposing groups.
This article asks which long-standing predispositions actually move voters' policy attitudes: core political values or more commonly cited anchors like partisanship and symbolic ideology. It focuses on two specific value orientations—traditionalist and egalitarian—and asks whether these values lead voters to change their issue stances over time, including on new issues that emerge in public debate.
Using the 2016 American National Election Study, the authors examine how Americans’ perceptions of immigrant threat and the strength of their American identity jointly relate to vote choice in the 2016 presidential election. Grounded in social identity theory, the paper tests whether perceptions of immigrant threat are more likely to translate into support for Donald Trump among those with stronger American identity, and conversely whether American identity’s effects on vote choice are activated by threat perceptions. With survey data from ANES, the authors report that immigrant-threat perceptions and American identity magnify each other’s effects: threat perceptions have larger associations with support for Trump among respondents with stronger American identity, and American identity has stronger associations with vote choice when respondents perceive greater immigrant threat.
New research investigates how increasing ideological polarization among American political elites influences public attitudes and behaviors.
Does supporting President Trump signal distinctive foreign policy views? We analyze this possibility.
A multinomial framework for ideal point estimation (mIRT) is developed using recent advances in Bayesian statistics. The core is a flexible multinomial specification that nests most common ideal-point models as "special cases," providing a single, unified representation for a wide class of models.
The use of draft lottery numbers (U.S. lotteries held 1969–1972) has become a common instrument in studies of political attitudes and behavior because, in principle, they provide true randomization of exposure to military service or its threat. However, the first lottery conducted in 1969 was not drawn randomly: citizens born in the fourth quarter of the year had disproportionately higher draft risks. This note explains that randomization failure and how it could, in theory, undermine the draft lottery’s role as an instrumental variable.
This study compares two common multiple imputation (MI) approaches: joint multivariate normal (MVN) MI, which models the complete data as a sample from a joint multivariate normal distribution and typically treats discrete categories as probabilistic draws from underlying continuous values; and conditional MI, which models each variable conditional on all others.
Valid comparisons of group scores on additive measures like political knowledge scales require that, after controlling for an individual's overall level on the latent trait, the conditional probabilities of responding to each item are invariant across groups. If this measurement invariance fails, apparent differences between subgroups may reflect measurement artifacts rather than true knowledge gaps.