Sunshine Hillygus awarded two large National Science Foundation grants

Monday, August 28, 2017

“Making Young Voters: Policy Reforms to Increase Youth Turnout”
$326,233 from Political Science Program, National Science Foundation, Sunshine Hillygus PI, John Holbein Co-PI, Matthew Lenard Co-PI
Voter turnout among young people is dismally low in the United States—often 20-30 percentage points lower than that of older Americans. This project evaluates potential education and electoral policies to increase youth turnout.  Bridging research in political science, education, and human development, we contend that increasing youth turnout requires not only lowering registration barriers for young citizens, but also helping them to develop the skills—especially the so-called noncognitive skills—needed to follow through and overcome the obstacles and distractions that get in the way of voting.  Building on an existing partnership with the Wake County Public School System, we bring together a diverse set of data sources to examine the early-life experiences, skills, motivations, and behaviors.  This research will help to inform a possible reconsideration of the nature of civic education in the United States, which primarily focuses on teaching facts and knowledge about government and politics. 
 
To evaluate potential education and electoral reforms to increase youth turnout this project brings together—for the first time—large-scale student surveys exploring early-life civic attitudes and behaviors, education administrative records from secondary and post-secondary schools that trace students’ formative experiences from very early in life to adolescence and beyond; a longitudinal database of state-level education and electoral policies that will allow us evaluate the effectiveness of existing policy approaches; in-school randomized-control interventions designed to increase youth registration; and public-use voter files.  These combined datasets will allow for a more comprehensive and rigorous evaluation of existing civic education and electoral policies to better inform the development of new reforms designed to promote voter turnout.

 

"Leveraging Auxiliary Information on Marginal Distributions for Survey Nonresponse"
$300K Methodology, Measurement, and Statistics Program, National Science Foundation, Jerry Reiter PI and Sunshine Hillygus Co-PI

This research project will develop methods and practical tools for leveraging the information from auxiliary data sources, such as administrative records and databases gathered by private-sector data aggregators, to adjust for nonresponse in surveys.  Modern surveys have seen steep declines in response rates.  These declines threaten the validity of secondary analyses based on those incomplete data.  Government agencies and survey organizations are under increasing budgetary pressures, however, and the result is fewer resources available for extensive nonresponse follow-up activities. In this environment, government agencies and survey organizations need new options for handling missing data.  This project will provide such options, enhancing the ability of data producers to create high-quality public use datasets that account for missing data.  The project will benefit data users, including scholars who use survey data and those interested in methods for evaluating and correcting for biases due to nonresponse.  An open-source package will be developed and made widely available via the Comprehensive R Archive Network. This package will enable agencies and other users to take advantage of the methodological advances.  The project will train two Ph.D. students from underrepresented groups, one in statistical science and one in political science. The project also will engage two undergraduate students in a data science summer research experience.

The methodological developments to be addressed in this project will focus on the following question: How can survey organizations take advantage of information about the marginal distributions of survey variables that are available in auxiliary data sources when adjusting for nonresponse?  The project will develop methods that enable users to posit distinct specifications of missing data mechanisms for different blocks of values. The project also will develop multiple imputation routines based on machine learning techniques to handle imputation with auxiliary information in databases with large numbers of variables.  The multiple imputation framework is leveraged to propagate uncertainty not only from the missing data, but also from population-based auxiliary marginal information with potentially non-trivial uncertainty.  The project will fuse features of Bayesian modeling and classical survey-weighted estimation to ensure imputations account for complex survey designs.  The methodology will be illustrated on an application examining voter turnout among subgroups of the population in the Current Population Survey (CPS).  The application will use population-based auxiliary data from government election statistics available in the United States Elections Project and voter files available from Catalist, a leading national vendor of voter registration data. The information in the auxiliary margins will be used to adjust the CPS data for nonresponse with a more reasonable set of assumptions than previous analyses of voter turnout based on the CPS.  The CPS voter turnout application will inform scholars and policy makers about inequalities in electoral participation and provides insights about possible policy alternatives for improving voter turnout.

 Watch Sunshine Hillygus serve on the US Census Bureau's Census Scientific Advisory Committee