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Research

2020 Southern Political Science Association Student Papers

The Florida Election Sciences group went on the road to San Juan, Puerto Rico to present our research at the 2020 Southern Political Science Association Conference. We had a rocking good time (literally) presenting undergraduate research projects:

Election Data Sciences Class Projects

Prof. Michael McDonald teaches a combined graduate and undergraduate class on election data science where students learn to analyze election data using the R programming language. Students post their final projects online, some of which they have presented at professional conferences. Follow the links to see the students’ Fall 2009 projects:

  • Emily Boykin <Link> This project seeks to explore the similarities and differences of state election systems, and offer descriptive and visual analysis of the data at hand. Utilizing the confidential voter files of U.S. states, I  analyze the different ways in which race, gender, birth identification, voter status, and party identification variables are classified by a sample of states. I utilize and read in .yaml files to code five different data frames on these variables, in which I map via a shapefile of the United States on a subjective key I created.
  • Steven Calcutt <Link> This is a study on how people convicted of a felony are registered to vote in the state of North Carolina. This study goes into their gender, race, age, and which party they were registered with. To control I used the general population of North Carolina in the voter file. I decided to do my project on this because of the impact that Amendment 4 could have on the electorate in Florida. It is important for anyone running a campaign to understand this data and apply it as necessary to their campaign strategy.
  • Sydney ElDeiry <Link> In this paper, I examine the effect that the shooting of Michael Brown by police officer Darren Wilson and the succeeding demonstrations had on voter registration in Ferguson and throughout St. Louis County, determining that communities can engage in politics through various avenues.
  • Delaney Goman <Link> A Georgia registration policy active in the 2018 midterm election was strikingly similar to a discriminatory policy struck down in 2016. An examination and analysis of the profile of applicants who failed the state’s restrictive exact-match policy and their turnout.
  • John Latimer <Link> My project explores the relationship between partisan identification and political trust. By utilizing organizational scholarship, I explain how partisan sorting might very well be the result of falling trust in the federal government. Though unable to show causation, the initial results are hopeful.
  • Peter Licari <Link> In this project, I use precinct-level data from the state of Florida — in addition to a small, custom dataset of active greyhound race tracks — to examine how precinct proximity to a greyhound track affects precinct-level support for an amendment which banned dog racing and precinct-level turnout on that issue. I found that proximity increases support for the amendment but depresses turnout.
  • Sara Loving <Link>  The goal of this project is to determine how the number of voters who register with a political party affiliation varies over an election cycle.
  • Madison Smith <Link> This project analyzes turnout data in Florida, 2016. Specifically, I am analyzing the effects of female candidates on turnout down the ballot.
  • Jenna Tingham <Link> This project evaluates Vote-By-Mail rejection rates and rejection likelihood at the county and state level in Florida. The primary objective is to study time predictors that lead to rejection, but the larger goal is to visualize and understand the Florida voter whose Vote-By-Mail ballot is rejected.
  • Madeleine Wagner <Link> I examine the strength of partisan transfer among Cuban American households in Miami-Dade and comparing it to the rest of the county. I study the basic level of homogeneity of Cuban households, as well as the level of full/partial/no transfer households and finally transfer by party.
  • Joel Zemach <Link> This analysis aims to demonstrate the effects of implementing Automatic Voter Registration (AVR) legislation on overall registration volume. I will use the Oregon state voter file to exemplify the effects of AVR on voter registration rates and will narrow my analysis to include data from 2015-2019 to capture the effects of this legislation. Automatic Voter Registration took effect in Oregon on January 1, 2016.

A Method to Audit the Assignment of Registered Voters to Districts and Precincts

Recently, elections in Georgia and Virginia were brought into question because some voters were given the wrong ballot – a number greater than the margin of victory. Recent Political Science PhD graduate Brian Amos and Professor Michael McDonald call this phenomenon administrative redistricting and develop a methodology to detect when it happens. They identify thousands of registered voters assigned to the wrong district and have been working with election officials across the country to proactively fix these problems. Their article on administrative redistricting is forthcoming in Political Analysis, the top political science methods journal.

Precinct Boundaries Project

One of our primary current activities funded by the Alfred P. Sloan Foundation and The Washington Post is to collect precinct boundaries for the entire United States and to merge them with precinct election results. This is a labor intensive project since in many places we must contact local election officials to obtain precinct boundaries and convert them into a usable format before we can combine them with election data. The data we are producing are available at a Harvard Dataverse Archive.  These data have numerous academic applications, and are being used by Dave’s Redistricting App and the DistrictBuilder software, the latter a project co-led by Prof. Michael McDonald, Micah Altman at MIT, the the software development company Azavea.

Creating Precinct Boundaries from Voter Files

What to do when we cannot obtain precinct boundaries from election officials? With support in part from the MIT Election Data and Science Lab we are developing a machine learning algorithm that maps precinct boundaries using geocoded voter registration addresses, each of which are associated to a precinct.

Accuracy of Voter Rolls

In 2019 we collaborated with the Virginia Division of Elections and in 2018 collaborated with the Colorado Secretary of State’s Election Division to develop tools to improve the accuracy of voter registration data.

Early Voting Statistics

In the run-up to the 2016 election we collaborated with the Associated Press to collect early voting data from states and localities. The AP hired two Election Sciences Group students to work as “stringers” to collect data. In 2018, we collaborated with Edison Media Research, which runs the media’s national exit polls, to collect these and other data.