Course Material for PSCI 8356: Statistics for Political Research 1
- Overview
- Required Applications and Resources
- Evaluation & Responsibilities
- Course Policies
- Office Hours
- Syllabus
- Helpful Resources
- Acknowledgements
This course will introduce students to the statistical methods used to study the political world. This is the first course in a sequence and assumes no background other than a passing knowledge of algebra and a little calculus. There will be an interactive lecture twice a week, as well as a section that meets with the TA approximately once a week. Questions are encouraged at all times; sections provide an opportunity to revisit the material and offer practice applying it.
The goal of the course is to provide students the tools to rigorously answer empirical questions in political science. We will begin with probability theory to hone our thinking about events that are inherently uncertain (as many that involve people are). Then we will progress through a series of topics that offer ways to detect and measure patterns in data, building toward an ability to use data to answer questions and to quantify the uncertainty in those measurements. Throughout the course, we will pay special attention to what counts as quality research and what is worthy of skepticism.
This course is designed to train political scientists to contribute to the very top of their field. We will use the statistical programming language R. Although the initial investment is larger than would be required to get up and running with software such as Stata or SPSS, the gains from greater flexibility (read: the ability to do more innovative research) and the ability to collaborate with other top scholars will yield high eventual returns. Likewise, problem sets are to be written up in LaTeX, a typesetting program that has become the industry standard for presenting rigorously conducted research.
Required:
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Wackerly, Mendenhall, and Scheaffer. Mathematical Statistics with Applications, 7th edition. Thompson. (WMS) PDF
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Wooldridge. Introductory Econometrics, 5th edition. Cengage. PDF
Suggested:
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Ethan Bueno de Mesquita and Anthony Fowler. Thinking clearly with data: A guide to quantitative reasoning and analysis. Princeton University Press, 2021.
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Charles Wheelan. Naked statistics: Stripping the dread from the data. WW Norton & Company, 2013.
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John Fox. Applied Regression Analysis and Generalized Linear Models. Sage Publications, 2015.
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Kieran Healy. Data visualization: a practical introduction. Princeton University Press, 2018.
This is the course management software used at Vanderbilt to support course learning. I will use this to post readings, lectures, assignments, and news for the course. I will post announcements and changes to the home page of the site; though I will always announce changes in class, please keep an eye out. If a change to the syllabus or requirements is posted in the announcements on this site, you are responsible for those changes.
Don’t forget to download the related app, which is called Pulse, to your phone and set it to alert you if there are new content or announcements for the course.
I have set up a Campuswire workspace for our use this semester to help us better communicate with each other and the TAs. You will need to create an account and join our workspace by following this link. You are encouraged to adopt these Slack etiquette tips.
Here is the list of channels you should see upon joining the Campuswire workspace:
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Class feed: A space to post questions and respond to other posts.
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#announcements: A space for all course announcements.
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#general: A space for you to share and discuss stories you've seen in the news or on social media that are relevant to our class.
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Calendar: A calendar containing all lectures, due dates, office hours, and labs.
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Files: A space for course materials (NOT USED. VISIT BRIGHTSPACE INSTEAD.)
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Grades: A space for grades (NOT USED. VISIT BRIGHTSPACE INSTEAD.)
I have created a GitHub repository to prepare and share all course-related content. This very syllabus is available as the repository's README and all links below are connected to the appropriate folders, sub-folders, and files in this repository.
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Participation: 10% Participating in lecture and section is expected. Constructive questions count.
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Problem Sets: 40% Problem sets will be issued regularly to offer practice applying the tools taught in class. You are welcome to work together on the problem sets as long as the writeup you turn is your own. The writeup must be done in LaTeX to earn credit. You can turn in one problem set for credit written using something other than LaTeX. All R code must be commented.
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** Exams:** 50% (25% each) The final exam will be comprised of two parts. The first is a presentation of the results of a data exercise. The second is a take-home exam. Details for both will be given in class. All material covered during the semester is eligible for the take-home exam; you may consult any non-person resource to complete it, but please work alone.
Problem sets and exams will have opportunities to earn extra credit, theoretically meaning you could score above 100%. All grades are curved without taking the extra credit results into consideration. We follow the standard Vanderbilt grading system, reproduced below:
- A: 94+
- A-: 90-93
- B+: 87-89
- B: 84-86
- B-: 80-83
- C+: 77-79
- C: 74-76
- C-: 70-73
- D+: 67-69
- D: 64-66
- D-: 60-63
- F: <60
Problem sets should be submitted via Brightspace. The problem sets are designed to require no more than six hours in total to complete. Late submissions will be penalized 1 point off for each day late. After three days, problem sets will no longer be accepted and will be scored 0.
You are asked to silence your cell phone / tablet / smart watch before class begins.
Students are assumed to have read and agreed with the Vanderbilt University Academic Honesty policy, found at URL: https://www.vanderbilt.edu/student_handbook/the-honor-system/
Collaboration is the heart of social science, but your work on your assignments should be your own. Please be careful not to plagiarize. In particular, while students are invited to work on problem sets together, collaboration is prohibited on the midterm and final exams.
Academic misconduct includes, but is not limited to, cheating, fabrication, plagiarism, altering graded examinations for additional credit, having another person take an examination for you, falsification of results, or facilitating academic dishonesty or as further specified in the university policy found at the website above. These and other forms of cheating are all potentially grounds for penalties including failure of the assignment or the course, as well as program- or university-level disciplinary action.
ChatGPT and related Large Language Models (LLMs) are essential tools in the social scientist's toolkit, and acceptable resources for completing the assignments and learning concepts at a deeper level. However, graded assignments cannot be generated purely by these tools. All assignments must include a log of the ChatGPT prompts and resulting output used to complete the assignment.
This class respects and welcomes students of all backgrounds, identities, and abilities. If there are circumstances that make our learning environment and activities difficult, if you have medical information that you need to share with me, or if you need specific arrangements in case the building needs to be evacuated, please let me know. I am committed to creating an effective learning environment for all students, but I can only do so if you discuss your needs with me as early as possible. I promise to maintain the confidentiality of these discussions. If appropriate, also contact the Vanderbilt Student Access office to get more information about specific accommodations; please visit https://www.vanderbilt.edu/student-access/ as soon as possible to become registered and ensure that accommodations are implemented in a timely fashion. Requests for academic accommodations are to be made during the first three weeks of the semester, except for unusual circumstances.
As per Vanderbilt's F22 Covid Plan, we are conducting class in as normal an environment as we’ve had since before the start of the COVID-19 pandemic. In general, this means:
- We will return fully to in-person instruction.
- Restrictions have been lifted on meetings and gathering sizes.
- Mask mandates are no longer in place.
- Asymptomatic testing has been suspended.
- Isolation guidelines only apply to those who have tested positive for COVID-19.
As such, DS1000 will be meeting in-person every Monday and Wednesday from 1:15PM - 2:30PM. Lectures will not be hybrid or remote. For students affected by COVID-19, this means that you are responsible for obtaining any course material you missed. As you'll see below, I make all materials available on the course GitHub repository. In addition, I will be recording all lectures and posting these the evening following the lecture. NB: this does not mean that students are permitted to skip the lectures.
I will be holding my office hours in-person in The Commons Center Room #353. The TAs are free to determine how best to hold their office hours, and their choices will be communicated to the students as soon as they are decided.
There are many things that you might be dealing with that can hinder your ability to succeed in this course, your college career, and your life. You might be struggling with illness, socioeconomic issues, or personal issues that make it hard to concentrate, to work, or to attend class. If any of these or other things begin to hinder your ability to do your best, you can reach out to the office of Student Care Coordination for programs, training, accommodations, and assistance (find more information or make an appointment here: https://www.vanderbilt.edu/carecoordination/). The Student Care Coordination can help guide you to whatever assistance you might need, whether it be short term or long term. If you specifically need help or accommodation in this course due to your difficulties, please come meet with me so we can find a solution that allows you to succeed while being fair to others.
Title IX makes it clear that violence and harassment based on sex and gender are Civil Rights offenses subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, etc. If you or someone you know has been harassed or assaulted, you can call the Project Safe 24-hour crisis/support hotline at 615-322-7233 and you can find a list of resources at Project Safe. You may also contact the University’s Title IX Coordinator (615-322-4705) and you can find the appropriate contacts for resources and confidence here: https://www.vanderbilt.edu/title-ix/
As a faculty member, one of my responsibilities is to help create a safe learning environment on our campus, no matter their identity or circumstances. I also have a mandatory reporting responsibility. It is my goal that you feel able to share information related to your life experiences in classroom discussions, in your written work, and in our one-on-one meetings. I will seek to keep information you share private to the greatest extent possible. However, I must note that I am a representative of an institution that we want to make safer for all people, therefore I am a mandatory reporter. University faculty, many staff members, and some student leaders are required to report incidents of sexual assault, sexual harassment, dating violence, domestic violence, stalking, and child abuse, as well as any suspected discrimination (about age, race, color, creed, religion, ancestry, national or ethnic origin, sex/gender, sexual orientation, disability, genetic information, military status, familial status or other protected categories under local, state or federal law) to the University’s Title IX Coordinator (615-322-4705), as required by University policy and state and federal law. If you disclose an experience of interpersonal violence and/or child abuse to me or to classmates with mandatory reporting, whether in class discussion, through a course assignment, or in private communication with me, your disclosure will be kept as private as possible but may not be able to be kept confidential.
Social science is, at its core, about thinking creatively to answer challenging questions. Creative thinking requires exposure to different perspectives, which are themselves borne of diverse experiences. I value diversity in all its forms including age, ability or disability, ethnicity, national origin, race, religion, sex, gender, sexual orientation, and family and marital status. I expect that all students participating in this class will respect differences and strive to understand how other peoples' perspectives, behaviors, and worldviews may be different from their own.
The observance of religious holidays (activities observed by a religious group of which a student is a member) and cultural practices are an important reflection of diversity. As your instructor, I am committed to providing equivalent educational opportunities to students of all belief systems. At the beginning of the semester, you should review the course requirements to identify foreseeable conflicts with assignments, exams, or other required attendance. If at all possible, please contact me within the first two weeks of the first class meeting to allow time for us to discuss and make fair and reasonable adjustments to the schedule and/or tasks.
- Prof. Bisbee: OH M & W at 3PM in Commons #353
- TA Gou: Recitation F at 10AM in Commons #363
All these can also be found on the Campuswire calendar, along with the Zoom links for those hosting their office hours remotely.
1. Introduction to Data Analysis: Aug 24 - Aug 29
2. Random Variables: Sep 5 - Sep 12
3. Multivariate Analysis and Expectations: Sep 14 - Sep 19
4. Sampling and Estimators: Sep 21 - Sep 28
5. Hypothesis testing and inference: Oct 3 - Oct 10
6. Midterm: Oct 12 - Oct 17
7. Relationships among RVs: Oct 24 - Oct 26
8. Inference, Errors, and Controls: Oct 31 - Nov 9
9. Confounders: Nov 14 - Nov 16
10. Final Things: Nov 28 - Nov 30
11. Final: Dec 7 - Dec 11
Each lecture's materials will be released according to the following schedule:
- Lecture Slides (PDF/html): Published the evening of the date of the class. PDFs can be downloaded. html slides can be viewed online.
Note that these links will return a 404 error if clicked prior to this release schedule.
To access lecture content and/or data sets use ctrl+click on a mac or right click on a pc, then click "save link as" and save to the class directory on your computer.
Lecture Content (2023/08/24): Welcome and overview
Lecture Content (2023/08/29): Intro to probability
No class (2023/08/31)
Lecture Content (2023/09/05): Discrete random variables
- Slides: PDF; HTML
- Homework: WMS chapters 3 and 4. Problem set 1 (due September 12)
Lecture Content (2023/09/07): Continuous random variables
Review Session (2023/09/12): Review sections 1 and 2
Lecture Content (2023/09/14): Multivariate analysis
Lecture Content (2023/09/19): Expectations workshop
- Slides: PDF; HTML
- Homework: Problem set 2 (due September 26)
Lecture Content (2023/09/21): Sampling
Lecture Content (2023/09/26): Estimators
Review Session (2023/09/28): Review sections 3 and 4
Lecture Content (2023/10/03): Hypothesis testing
- Slides: PDF; HTML
- Notes: PDF
- Homework: WMS chapters 9 and 10. Problem set 3 (due October 10)
Lecture Content (2023/10/05): Power
Lecture Content (2023/10/10): P-values
Review Session (2023/10/12): Review of first half of semester.
Midterm Exam (2023/10/17): In-class exam
Lecture Content (2023/10/24): Relationships among RVs
- Slides: PDF; HTML
- Homework: Wooldridge chapter 2. Problem set 4 (due November 7).
Lecture Content (2023/10/26): The slope and intercept of a regression line
Lecture Content (2023/10/31): Inference
Lecture Content (2023/11/02): Error variance and controls
Lecture Content (2023/11/07): Error variance and controls
- Slides: PDF; HTML
- Homework: Problem set 5 (due November 28)
Review Session (2023/11/08): Review sections 7 and 8.
Lecture Content (2023/11/14): MATRIX FUN TIMES WOO HOO!
Lecture Content (2023/11/16): Sampling distribution of estimates
Holiday: (2023/11/21)
Holiday: (2023/11/23)
Lecture Content (2023/11/28): Interpreting Regressions
- Slides: PDF; HTML
- Homework: Wooldridge chapters 6 and 7. Problem set 6 (due December 5)
Lecture Content (2023/11/30): Regression Diagnostics
Lecture content (2023/12/05): Data exercise presentations part 1
Lecture content (2023/12/07): Data exercise presentations part 2
Final Exam (2023/12/11): Take Home Exam due 12/11/2023
Rstudio Cheat Sheet: Data Wrangling
... And the full list of Rstudio cheat sheets
The contents of this course and of my teaching pedagogy are influenced and inspired by:
- Jenn Larson, Vanderbilt University
- Patrick J. Egan, New York University
- Emily Hencken Ritter, Vanderbilt University
This course is modeled on the course of the same name, taught by Professor Jenn Larson in the fall of 2022 at Vanderbilt University, and on Quant 1 at NYU, taught by Professor Patrick J. Egan. The syllabus is heavily inspired by Emily Hencken Ritter's syllabi for PSCI 3270, Politics of Human Rights.