Required Text

John Sides, Daron Shaw, Matt Grossmann & Keena Lipsitz. Campaigns and Elections. New York: W. W. Norton. ISBN 978-1-324-11504-5.

Readings from the text provide the context for each week; our class time is spent working with data. Expect to read one chapter per week before the Tuesday session, and to spend Thursday in a hands-on lab building something from real electoral data in R. Chapters are short — budget about an hour.

Additional readings are posted on Canvas. There is nothing else to buy.

Software

This is not a programming course, and there is no coding prerequisite.

Labs run in R, using RStudio. Both are free, and we install them together in the first week. You will never be asked to write a program from scratch. Every lab arrives as working code with the results already in front of you; your job is to run it, change a setting or two, and above all explain what you are looking at. If you have never seen a line of code, you are exactly who these labs are written for.

What you are graded on is the thinking, not the typing. A lab write-up that changes one number and draws a sharp, well-supported conclusion earns more than one that produces an elaborate chart and says nothing about it.

Nearly everything we do uses R as it comes out of the box, with no add-on packages. Late in the term we install two or three for mapping Census data, and we do that together in class. Bring a laptop to Thursday sessions if you have one; if you do not, tell me and we will sort it out.

Course Description:

American democracy is rich with data — from historical vote tallies to modern polling, turnout, and campaign finance. In this course, we’ll investigate how democracy functions by analyzing this data, uncovering the political, social, and structural forces that shape electoral outcomes. Students will engage with historical case studies (e.g., 1876, 1960, 2000) alongside contemporary elections to see how past events illuminate present dynamics. Through lectures, labs, and projects, students will gain the tools to collect, analyze, and interpret electoral data — and apply these skills to real-world political questions.

Course Goals

This course has one central aim: that you leave able to look at a number about American democracy and know what to ask of it. Who produced it, what it counts, what it leaves out, and whether it supports the claim being made in its name.

Everything else — the readings, the labs, the projects — serves that.

Learning Objectives

By the end of the semester you will be able to:

1. Explain where American election data comes from — who produces it, at what geographic level, and what each source does and does not capture. (Units I and V)
2. Read a table, chart, or map of electoral data and say plainly what it shows, what it does not show, and what would have to be true for its conclusion to hold. (every unit; this is the core skill)
3. Test a claim about elections against the underlying data — whether the claim comes from a news story, a campaign, or a scholarly argument. (Units II and III)
4. Explain the institutions that convert votes into power: the Electoral College, apportionment, districting, and the Voting Rights Act. (Units I, IV, V)
5. Recognise when a number misleads — through selective framing, mismatched units of analysis, or a model that gets the right answer for the wrong reason. (every unit)
6. Write about quantitative evidence for a non-technical audience, in both journalistic and policy forms. (the two projects)

Each unit below states which of these it is building. If at any point you cannot tell why we are doing something, that is a failure of my design and not of your attention — please say so, and I will fix it.

How This Course Works

The same rhythm every week. Nothing in the schedule should ever surprise you.

The lab write-up is the heart of the course. It is short — a page is typically enough — and it has two parts:

• (a) The reading. A paragraph. What did the chapter claim, and did the data we worked with support it, complicate it, or fail to speak to it?
• (b) The data. Change something in the lab — a year, a state, a column, a threshold — re-run it, and tell me what changed and why it matters. The code is given to you. The writing is the assignment.

Assessments

• Participation & Attendance – 15% Active engagement in discussion and labs.
• Weekly Lab Assignments – 40% The weekly write-up described above.
• Weekly Data Contributions – 10% Submit one relevant data point related to politics or democracy, accompanied by a brief explanation and visualization to illustrate its significance.
• Data Journalism Project – 15% Produce a data-driven news story that investigates a significant trend, pattern, or issue in electoral processes.
• Final Project: Policy Proposal – 20% Develop an evidence-based policy proposal that addresses a specific issue in the electoral process.

Structure of the Course

The semester runs in five units. Tuesdays introduce the week’s question and the reading; Thursdays are labs, worked in R.

Unit I — Foundations. What counts as election data, where it comes from, and how the American electoral process is put together. (Ch. 1–2)

Unit II — The Campaign Machine. How campaigns transformed, who pays for them, how they target voters, and the role of parties and organized interests. (Ch. 3–7)

Unit III — Media and Voters. Media effects and partisan media; who turns out, and how voters decide. Polling, sampling, and forecasting. (Ch. 8, 12–13)

Unit IV — The 2026 Election. We meet two days after the midterms to compare results against forecasts using live returns. (Ch. 9–11)

Unit V — Representation: Who Counts, and Who Is Counted. The Census, apportionment, the Voting Rights Act, racially polarized voting, and redistricting. This unit goes beyond the textbook and draws on primary sources and original data.

Assignments

1. Current Events Data Snippets:

   • You must submit one piece of relevant data on the Canvas discussion board (slide on link provided) before class on Tuesday.
   • You can access free articles and archives from the New York Times and other major newspapers using your cmu.edu email.
   • You may be called on randomly to share your data nugget.
   • I will create a shared powerpoint where you can post your slide.
   • They will be graded on a completion basis (complete/incomplete).

   Example:
   Voter Turnout: About two-thirds (66%) of the voting-eligible population turned out for the 2020 presidential election ¹(https://www.pewresearch.org/politics/2023/07/12/voter-turnout-2018-2022/).
   Create info-graphic visualizations to illustrate the data, to be posted on common slideshow. Link is provided on Canvas

2. Weekly Lab Assignments

   • We work through a lab together in R during Thursday’s session. You submit what you have at the end of class; this doubles as attendance.
   • By the following Tuesday, submit the short write-up described above: a paragraph on the reading, and an account of what you changed in the lab and what it showed.
   • You are not asked to write code. Every lab arrives working. Changing a year or a column is a one-word edit, and I will show you exactly where.
   • If you do want to go further — write something of your own, bring in another dataset — that is welcome and is one route to a 4. It is not required, and you can earn full marks without it.

   Write-ups are graded on a four-point scale:

   Score	What it looks like
   4	Everything in a 3, plus something I did not ask for: a caveat about what the data cannot show, a comparison to another week, or a question the result raised.
   3	The change was made and re-run correctly, and you explain what it showed and why it matters. This is the target. A 3 is good work.
   2	The output is there but the interpretation is thin — description without a point.
   1	Incomplete, or the interpretation does not follow from the output.

   A semester average of 75% earns an A−. Do not chase 4s; a consistent 3 is a strong grade.

3. Data Journalism Project: This project requires you to write a compelling, data-driven news story investigating a significant electoral trend, pattern, or issue. You’ll combine original data analysis, at least two visualizations, expert interviews, and historical context to create an engaging narrative that explains how and why democracy functions as it does.

4. Policy Proposals:
   This assignment asks you to develop a clear, evidence-based policy proposal addressing a specific electoral process issue such as voter turnout, election security, or poll accuracy. You’ll use data, historical analysis, and real-world examples to identify the problem, propose a solution, and justify its effectiveness for policymakers.

5. Attendance:
   Regular attendance and active involvement form a significant part of your final grade (see grading section). If you do not show up, you will not earn an ‘A’. Participation is not just about being present; it involves engaging with the material, contributing to discussions, and collaborating with your peers. To recognize that occasional absences are sometimes unavoidable (e.g., for religious observance, job interviews, university-sanctioned events, or illness), attendance grades will be calculated using an exponential function. 1–2 absences → mild penalty, 6+ absences → sharp drop (serious consequences).

Effect of Absences on Grade

Students are expected and encouraged to meet all deadlines for assignments. If you are unable to complete the assignment work by the due date, reach out in advance to make alternative arrangements. I typically will not penalize you for turning in your assignment late, so long as it does not hinder completion of other`s work (ie, group projects).

The course grade will be a weighted average of the following components:

Schedule

Classes meet Tuesday and Thursday, 11:00a–12:20p, in PH A21A. Tuesdays introduce the week’s question and discuss the reading; Thursdays are labs. Read the assigned chapter before Tuesday. Lab write-ups are due the following Tuesday.

Subject to change as the semester progresses.

AI Use Policy for Student Work

As artificial intelligence (AI) tools become increasingly accessible, it is important to clarify expectations for their use in this course. You are welcome to use AI technologies (such as ChatGPT, Grammarly, or similar tools) to support your independent work—such as brainstorming ideas, checking grammar, or improving the clarity of your writing. However, you may not use AI to generate substantive content that you submit as your own original work. All assignments, essays, and projects must reflect your own analysis, critical thinking, and voice.

Permitted Uses of AI:

• Outlining or organizing your thoughts
• Checking grammar, spelling, or clarity
• Generating ideas or prompts to help you get started
• Reviewing your own drafts for readability

Prohibited Uses of AI:

• Submitting AI-generated essays, paragraphs, or answers as your own work
• Using AI to complete assignments, discussion posts, or projects in place of your own effort
• Copying and pasting AI-generated content without substantial revision and personal input

If you use AI tools in your process, you must disclose how you used them in a brief note at the end of your assignment (e.g., “I used ChatGPT to help brainstorm ideas for my outline.”).

Violations:
Submitting AI-generated content as your own is considered academic dishonesty and will be treated as a violation of the university’s academic integrity policy.

If you have questions about what is or is not allowed, please ask before submitting your work.

Representation Statement

I am committed to including a broad range of perspectives in the readings and materials for this course. If you believe a critical voice is missing, please let me know so I can improve the syllabus now and in future offerings.

We must treat every individual with respect. We come from many different backgrounds, and this variety of viewpoints is fundamental to building and maintaining an equitable and inclusive campus community. “Representation” can refer to the ways we identify ourselves—race, color, national origin, language, sex, disability, age, sexual orientation, gender identity, religion, creed, ancestry, belief, veteran status, or genetic information, among others. Each of these identities shapes the perspectives our students, faculty, and staff bring to campus. Promoting these varied viewpoints not only fuels excellence and innovation but also advances the pursuit of justice. We acknowledge our imperfections while fully committing to the work—inside and outside our classrooms—of building and sustaining a campus community that embraces these core values.

Each of us is responsible for creating a safer, more inclusive environment.

Unfortunately, incidents of bias or discrimination do occur, whether intentional or unintentional. They contribute to an unwelcoming atmosphere for individuals and groups at the university. Therefore, the university encourages anyone who experiences or observes unfair or hostile treatment on the basis of identity to speak out for justice and seek support—either in the moment or afterward. You can share your experiences using the following resources:

• Ethics Reporting Hotline
  Submit an anonymous report by calling 844-587-0793 or visiting cmu.ethicspoint.com.
  All reports are documented and reviewed to determine whether further action is needed. Regardless of the incident type, the university will use your feedback to transform our campus climate into one that is more equitable and just.

Accommodations for Students with Disabilities

If you have a documented disability and an accommodations letter from the Office of Disability Resources, please discuss your needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect you may have a disability and are not yet registered with the Office of Disability Resources, you can contact them at access@andrew.cmu.edu.

Student Well-Being

The past few years have been challenging. We are all under significant stress and uncertainty. I encourage you to find ways to move regularly, eat well, and reach out to your support system—or to me at cervas@cmu.edu—if you need help. We can all benefit from support during stressful times, and this semester is no exception.

As a student, you may experience a range of challenges that interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating, or lack of motivation. These mental health concerns or stressful events can diminish your academic performance and reduce your ability to participate in daily activities. CMU offers services that can help, and treatment does work. Learn more about confidential mental health services available on campus at:

• Counseling and Psychological Services: http://www.cmu.edu/counseling/
  Phone (24/7): 412-268-2922

Please remember that support is always available—don’t hesitate to reach out.

Major Debates in American Politics: Reference Sheet