JUNIOR SEMINAR: Data and Elections

How do we know how supportive the public is of a president, whether they support existing or new policies, or otherwise measure the pulse of the nation?  How do campaigns decide where to send volunteers, where to buy advertising, or who is most persuadable?  How can we determine the effect of audits or observers on electoral fraud?  This class will investigate how data is used by campaigns during elections, as well as how data is used to study the impact of campaigns and events on elections.  “Big data” is powerful and how to understand and best use data in decision-making is rooted in basic statistical principles.  This class will focus on three core concepts in statistical inference, descriptive, predictive, and causal inference.  We will study descriptive inference through survey sampling and measuring public opinion; predictive inference through how campaigns use statistical (“machine”) learning to allocate resources; and causal inference through experimental and observational studies of electoral fraud and fairness.  This course will include an intensive data analysis portion.  Based on skills we learn in this class, students will analyze surveys, build predictive models, and conduct and interpret data analyses using the R programming language.  Students will conduct their own original analysis in a final project.

Junior seminars fulfill upper division requirements for the major.

Subfield:   Methods