Professor Jonathan Cervas
Office: Posner Hall 374
Email: cervas@cmu.edu
Location: Porter Hall A21A
Time: Tuesday & Thursday 11:00a-12:20p Eastern
Office Hours: Wed 10:30a-12:30p & 1:30p-3:30p, and by appointment (arrange via email)
CMU Academic Calendar
Course Relevance: DC: Perspectives on Justice and Injustice Learning Resources: All resources will be provided via Canvas
Prerequisites: 36-200 Reasoning with Data
25 meetings, Tuesdays and Thursdays. One of them, Tue, Nov 24, meets on Zoom rather than in the room. Tinted days are ones we do not meet — fall break, Democracy Day, Thanksgiving, and the Constitution Day Symposium.
The point of this course is to understand the political world — particularly through the data it produces. American elections generate an enormous amount of it: vote returns reaching back to the nineteenth century, the census that decides how many seats each state gets, campaign finance filings, polls, roll-call votes, and the record of who turned out and who did not. Every one of those numbers was produced by somebody, for a purpose, and every one of them leaves something out.
We work with real data every week — 41 presidential elections since 1864, the apportionment that followed the 2020 census, FEC filings, congressional roll calls, Census and American Community Survey estimates, and the 2026 midterms as they happen.
This is a general education course and there is no coding prerequisite. Every data exercise we do arrives as a prepared data brief with the results already in front of you. What you are asked to do is read it closely, follow how the numbers were made, and explain what you are looking at. The skill being built is not programming; it is judgment about evidence. You will practice it on a data-driven news story written for real readers, and defend it in class every week.
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 project — serves that.
By the end of the semester you will be able to:
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. Expect to read one or two chapters before Tuesday, when we discuss it, and to spend Thursday working with data — the slides you brought, then the lab. Chapters are short — budget about an hour.
There is nothing else to buy.
The textbook is the context; the news provides content. A chapter is settled by the time it prints, and this one went to press before the campaign you are living through.
So most weeks carry one or two short current-events items alongside the chapter — an article, an interactive, a newsletter post, sometimes a podcast. They are listed in the schedule. They are not optional: they are usually what Tuesday actually argues about, because they are where the chapter’s claim either holds up or does not.
Expect additions. A syllabus written in August cannot know what November will produce. If the schedule and Canvas disagree, Canvas is right.
Current events are also where your weekly data slide comes from. You are not expected to go hunting in unfamiliar places. The sources below are valuable resources, but you can find other things as well.
| Worth following all term | |
|---|---|
| General | New York Times (especially The Upshot), Washington Post, Wall Street Journal — all three are free to you through the CMU Libraries |
| Elections specialists | Bolts, Votebeat, Stateline, Democracy Docket, Cook Political Report, Sabato’s Crystal Ball |
| The census | Count of Counting — Mike Schneider, who covered the Census Bureau for the Associated Press, on population change and the count itself |
| Data and forecasting | Strength In Numbers (G. Elliott Morris), Silver Bulletin, Split Ticket, The Downballot, Playing with Election Data (Charles Stewart) |
| Podcasts | The Ezra Klein Show (NYT) · NPR Politics Podcast · The Downballot (weekly, Thursdays) · Amicus (Slate) · Public Opinion Podcast (AAPOR) |
This is not a programming course, and there is no coding prerequisite.
There is no software to install and nothing to set up. Every data exercise or example arrives as a prepared data brief — the source, the decisions behind it, and the figures already made — and your job is to read it, follow how the numbers were built, and above all explain what you are looking at. If you have never worked with data before, you are exactly who these are written for.
Bring a laptop to Thursday sessions if you have one; the briefs are web pages and they read well on a phone or on paper too. If you would rather work from a printout, say so and I will bring one.
Two things run side by side. One is the textbook: how campaigns and elections work, read straight through from Chapter 1 to Chapter 13. The other is the data — where the numbers about American politics come from, who produced them and why, and what they can and cannot tell you. Generally speaking, Tuesday is the book. Thursday is the data.
The two are not synchronized, and are not meant to be. The textbook keeps its own order, and the three tests cover it and nothing else. The data sessions run their own sequence, below.
| When | What |
|---|---|
| Before Tuesday | Read the week’s chapter — about an hour. |
| Tuesday, in class | The chapter, and the question it raises. |
| Before Thursday | Post a data slide bearing on the question we ended Tuesday with. |
| Thursday, in class | Two of your slides, then the lab. |
| By Friday, 11:59 p.m. | One post to the discussion board about any data we looked at that week. |
Eleven sessions in three parts, and the parts are a claim about who collected the data and why. The state enumerates, and you are required to answer (I). Somebody asks, and you answer because you feel like it (II). Then an election happens, and Part III is everything it leaves behind — the result certified, the machinery administered, a donation disclosed because a statute compels it, a voter file nobody volunteered for, and, where nobody collected anything, a number a researcher built.
| Pt | Sessions | |
|---|---|---|
| I | The Census Bureau | 1–3 |
| II | Surveys | 4–5 |
| III | Elections, and the records around them | 6–11 |
The course grade will be a weighted average of the following components:
| Category | Percent of Final Grade |
|---|---|
| Participation & Attendance | 23% |
| Discussion Board | 10% |
| Surveys (3, completion only) | 2% |
| Data Slides | 10% |
| Three Tests (10% each) | 30% |
| Data Journalism Project | 25% |
Absences and the attendance grade. The
first three cost almost nothing; four to six is where it
bites.
Full instructions for every assignment are in Canvas, under Assignments. What each one is, in the order of the table above:
| Assignment | Due |
|---|---|
| Data slide | before each Thursday |
| Surveys (3) | Wed, Aug 26 (Cervas Election Study), Fri, Oct 23 (midterm feedback), final week (course evaluation) |
| Discussion board post | Friday, 11:59 p.m., each week |
| Week 4 board post (no Thursday class) | Wed, Sep 16 — day after the Tue Sep 15 class; Thu Sep 17 is Constitution Day, no class |
| Week 14 board post (Thanksgiving) | Wed, Nov 25 — day after the Tue Nov 24 Zoom class; Thu Nov 26 is Thanksgiving, no class |
| Test 1 — Ch. 1–5 | Thu, Oct 8, in class |
| Data journalism: pitch | Tue, Oct 20, in class |
| Test 2 — Ch. 6–9 | Tue, Nov 10, in class |
| Data journalism: draft | Thu, Nov 19 — the term’s last data session |
| Data journalism: peer reviews (two) | Tue, Dec 1 |
| Test 3 — Ch. 10–13 | Thu, Dec 3, in class |
| Data newsletter | Tue, Dec 8 |
| Data journalism: final | Fri, Dec 11 — after the last session |
There is no final exam.
Your grade rests on engagement as much as on output. The course is built around two things happening every week — you arriving having read, and you arriving having found data — and neither can be made up afterwards.
Deadlines. You are expected to meet them. If you can see in advance that you will not, contact me before the due date and we will sort something out.
Late work loses one percentage point per hour, and never falls below 50%.1 Canvas applies this automatically. Submit what you have rather than polishing something that is already late.
Work that is never submitted scores zero, which is the whole reason the late floor sits at 50%: handing something in a week late is always worth more than handing in nothing.
Two things sit outside that rule because being late breaks somebody else’s work, not just your own:
Week 8 — FALL BREAK, no class (Oct 12–16) Test 1 is behind you. Nothing is due.
Finals period — there is no exam.
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:
Prohibited Uses of AI:
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.
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:
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.
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:
Please remember that support is always available—don’t hesitate to reach out.
An hour or two costs almost nothing; a day costs a letter grade. Charging by the hour rather than the day removes the cliff at midnight — being twenty minutes late should not cost the same as being twenty hours late. The floor is there so that late work is always worth more than no work.↩︎