Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.

Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Affective Forecasting and Policy Capturing: Modeling Political Judgment

Colin P Holloway, University of Nebraska - Lincoln


The American political landscape is defined by a two-party system of government with elites on either side of the ideological divide influencing a wide range of electoral behaviors. Voters use partisan affiliation as source of information, as a characteristic of their identity, and as a guide on how to understand and process social and political events. Although the influence of political affiliation on voter behavior is well documented, there are a subset of political decisions over which parties might hold less sway over individual choices. As American voters drift towards political independence and party elites engage in highly publicized intra-party debate on important policy issues, it is possible that the voting public becomes less responsive to partisan influence when considering whether to support specific policies. Two studies tested the boundaries of partisan affiliation on political decision-making by exploring the effect of voter emotions on political support for policy proposals. In Experiment 1 voters were presented with one of two policies, the criminal justice reform First Step Act and a policy of increased military aggression against Iran before reading an article designed to induce positive or negative emotions. Results showed that voters in the Iranian aggression condition were not responsive to emotional influence and instead voted consistent with party affiliation; however, voters in the First Step Act condition were more influenced by their emotional response to the policy than partisan loyalty. Experiment 2 focused solely on the First Step Act and took a policy capturing approach to modeling political judgment by creating a series of regression models which predicted political behavior using party affiliation, predicted emotions, and utilization of policy-relevant cues. Results showed that voter affective forecasts regarding the passage of the First Step Act were the most robust predictors of voter support. Taken together, these studies demonstrate that voters are more responsive to their emotions than to party loyalty, a finding which has implications for the study of political sub-groups and intra-party divisions.

Subject Area

Social psychology|Behavioral psychology|Political science

Recommended Citation

Holloway, Colin P, "Affective Forecasting and Policy Capturing: Modeling Political Judgment" (2020). ETD collection for University of Nebraska-Lincoln. AAI28092512.