Brittany Alexander, ISTPP Predoc Fellow and doctoral student in statistics, presented her paper, “Understanding Terrorism Policy Preferences through Bayesian Model Averaging and Multiple Imputation,” at the Southern Association for Public Opinion Conference on October 1, 2020, and at the Midwest Association for Public Opinion Conference on November 20, 2020. ISTPP Director Arnold Vedlitz coauthored this study.
Their research uses a two-wave panel survey conducted by ISTPP in 2016 with funding from the National Science Foundation. The survey measured a variety of issues and attitudes, including perceived likelihood of terrorism, different concerns about terrorism, support for federal and local spending, and support for specific polices to prevent terrorism.
The researchers used Bayesian model averaging to create linear models of the change in perceived likelihood of terrorist attacks, support for policies, and support for federal and local spending on counterterrorism measures. They found that views on terrorism remained relatively stable across the two waves of the survey, but the change could be predicted using psychometric variables in the survey.
Additionally, new policies for terrorism were generally supported, and more people supported a very high or pretty high level of federal and local spending to prevent terrorism over a very low or somewhat low level of spending.