Hierarchical models for estimating state and demographic trends in US death penalty public opinion

Kenneth E. Shirley and Andrew Gelman

Journal of the Royal Statistical Society Series A, 2015, vol. 178, issue 1, 1-28

Abstract: type="main" xml:id="rssa12052-abs-0001">

One of the longest running questions that has been regularly included in US national public opinion polls is ‘Are you in favor of the death penalty for persons convicted of murder?’. Because the death penalty is governed by state laws rather than federal laws, it is of special interest to know how public opinion varies by state, and how it has changed over time within each state. We combine dozens of national polls taken over a 50-year span and fit a Bayesian multilevel logistic regression model to estimate support for the death penalty as a function of the year, the state, state level variables and various individual level demographic variables. Among our findings were that support levels in northeastern and southern states have moved in opposite directions over the past 50 years, support among blacks has decreased relative to non-blacks, but at slightly different rates for men and women, and support among some education groups varies widely by region. Throughout the paper, we highlight the use of a variety of analytical and graphical tools for model understanding, including average predictive comparisons, finite population contrasts for overparameterized models and graphical summaries of posterior distributions of group level variance parameters.

Date: 2015
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