Quantitative Political Analysis
Office hours: Th 9:30-11:30 on Calendly
Class schedule: K and L
I know this document is really long, but please read it! Especially the parts I’ve colored or highlighted or ⭑ bulleted.
Table of Contents
Political scientists frequently use quantitative methods to address questions about elections, wars, beliefs and attitudes, policy outcomes, and other important social and political phenomena. This course will consider the general concepts underlying empirical research, including causal inference, research design, statistical analysis, and programming. The goal is to help students become informed consumers of quantitative social science research and provide them with useful tools for undertaking empirical research of their own. There are no formal prerequisites for this course.
By the end of this course, students should be able to:
I have found that the best way to learn statistics and programming is to “learn by doing.” We will therefore often combine traditional lecturing with a hands-on approach. Each week you will complete readings mostly from the textbook and take a learning assessment quiz about the content before you come to class. Then, during synchronous class sessions, I will explain and review the statistical concepts and there may also be some group exercises. My lessons on coding in R will be recorded and provided asynchronously, so that you can easily follow along on your own, pause when you need to, and see my code clearly without the distraction of Zoom. You’ll watch these videos on your own time, but it’s recommended that you do so after we’ve covered the corresponding statistical concept in class so that you can fully grasp what the code is doing.
I am teaching two sections of this course in Spring 2021: K (2:50-4:40 EST) and L (5:00-6:50 EST). You are welcome to come to either (or both!) of the class sessions, regardless of which section you are officially enrolled in. However, if you’re enrolled in section L but attend section K, you’ll have complete your quiz before the start of section K. We’ll talk more about this during the first week.
This class will be taught remotely. Synchronous sessions will be held on Zoom during our scheduled time in Eastern Standard Time. I ask you to keep your camera on during our Zoom sessions because some evidence suggests that people are more engaged in virtual calls when their cameras are on. Feel free to use a virtual background. With that said, I understand that there might be instances when you might prefer to have your camera off. Please email me if this is the case.
Obviously, we are still operating under exceptional circumstances. You never owe me personal information about your health (mental or physical) or family situation. That said, if you need extra help with something, please ask. I will work with you to figure out a solution that does not compromise fairness for everyone, but that also prioritizes your health, personal well-being, and education as much as possible. If you tell me you are struggling with something, I will not think less of you. I hope you’ll extend the same grace to me.
Synchronous class sessions will be recorded at my discretion. Any recordings will be posted to Canvas. Dartmouth’s recording policy will apply to this course. By enrolling, you affirm your consent to the following: “By enrolling in this course, a) I affirm my understanding that the instructor may record meetings of this course and any associated meetings open to multiple students and the instructor, including but not limited to scheduled and ad hoc office hours and other consultations, within any digital platform, including those used to offer remote instruction for this course. b) I further affirm that the instructor owns the copyright to their instructional materials, of which these recordings constitute a part, and my distribution of any of these recordings in whole or in part to any person or entity other than other members of the class without prior written consent of the instructor may be subject to discipline by Dartmouth up to and including separation from Dartmouth. By enrolling in this course, I hereby affirm that I will not make a recording in any medium of any one-on-one meeting with the instructor or another member of the class or group of members of the class without obtaining the prior written consent of all those participating, and I understand that if I violate this prohibition, I will be subject to discipline by Dartmouth up to and including separation from Dartmouth, as well as any other civil or criminal penalties under applicable law. I understand that an exception to this consent applies to accommodations approved by SAS for a student’s disability, and that one or more students in a class may record class lectures, discussions, lab sessions, and review sessions and take pictures of essential information, and/or be provided class notes for personal study use only.” If you have questions, please contact the Office of the Dean of the Faculty of Arts and Sciences.
The following textbook is required.
Alan Agresti. Statistical Methods for the Social Sciences. 5th edition. Pearson.
The first two chapters will be posted on Canvas for students who do not yet have the book. You will need your own copy in time to do the readings for Week 3. If you want to use a version of the textbook other than the exact one listed above, you are responsible for any material that is missing or different from the fifth edition. I will not be able to tell you how the textbooks differ; I teach from the fifth edition, and that is the edition I know.
I like this textbook the best for several reasons, but it is suggested you use other resources to supplement your learning (see the following two sections). As political scientist Chris Achen once wrote about statistics courses, “Ah, the textbook. You will almost certainly dislike the text – virtually every student does, no matter which book is chosen... No text works well for everyone, and no text works well all the time for anyone.”
Other materials like lecture videos will be posted on Canvas. I also recommend using as many other resources as you need to supplement the required materials. Here are a few I have found to be helpful:
Tutoring Groups will be offered for this class! To learn more about how Group Tutoring works, please visit the Tutor Clearinghouse website. Stay tuned for details on how to register for Group Tutoring.
Studying is a skill that requires learning and takes practice. I highly recommend that you read Vox’s guide to improving how you prepare for exams.
We will use the R programming language in this course. You will execute all programming code within the RStudio environment and use RMarkdown for all problem sets. We will go over what this means during the first class. You should have R, RStudio, and RMarkdown working on your computer and ready to use by the second week of class. There is a short assignment (due by the end of 4/6) on Canvas with step-by-step instructions to make sure you are all set up and ready to roll.
Your ability to search for and find help independently is an extremely important component of learning a statistical programming language. If you have problems using R or with statistical analysis throughout the term, please consult the following resources in this general order:
This course will have a Slack workspace which will be a mandatory and essential part of class. If you are new to Slack, that’s okay. You’ll get an invite to join the workspace at the start of the term. If you have issues setting it up, contact me or ITC (https://itc.dartmouth.edu/support). I will send all correspondence via Slack and/or Canvas. You are responsible for checking your email, Slack, and Canvas for any notifications, announcements, and course discussion. I strongly recommend getting the integrated Slack app for your computer and/or smartphone so you can easily check and make posts.
Download the Slack Desktop App:
If you see a post from a classmate asking a question, I strongly encourage you to try to help. I will not think less of you if you try to help and post an incorrect answer. If you do not know the answer to someone’s question but are having the same issue, let us know! This isn’t an opportunity to get others to complete your work for you, so it is best to stick to questions to troubleshoot specific issues, rather than, “How do we complete this question in the problem set?” But in general, make an effort to reply to and communicate with one another. This will go a long, long way in building community for our course and making this term a great experience for everyone. Let’s all suffer (I mean... learn) together! (Not to mention, participation in the course, including on Slack, is a small portion of your final grade).
Note that having a course Slack does not mean I am available to answer questions at all times of the day or night. I do typically reply to Slack quicker than email, but I still usually stick to regular business hours (M-F 9-5).
You are welcome and encouraged to ask questions about the material, especially the readings and lectures, in class, on Slack, or in office hours. However, there will also be an anonymous form for the course in which you can ask questions anonymously. For R questions, please use the corresponding Slack channel instead. I will try to answer the submitted questions during class. In the case I don’t get to your question, try asking on Slack or in office hours instead, or even during class time during lecture since it’s sometimes easier to prioritize questions that are asked “live.”
Office hours are designated times faculty members set aside each week specifically for students to visit. These hours are your chance to ask questions about the course material or college in general in a one-on-one setting. If you need help with something, these meetings work best if you have studied or attempted to solve a problem and discovered what you need help with before coming to my office. Then, when you come in with specific questions, I can try to explain a different way or help you work through a problem.
My office hours are scheduled in advance. You can book any time listed on my Calendly page, even if it is outside my normally-listed office hours. If no slots are available, please Slack DM to figure out an alternate meeting time. Feel free to book as a group, also (i.e. if your classmates/research team have the same questions), but please tell me who is coming in the Calendly form.
A lot of students tend to meet with me, especially for this course. It is not uncommon for me to be completely booked through every single week. I don’t say this to turn you away: the fact that I am booked means that students are meeting with me, so you should feel welcome to, too! But for some types of questions, you should consult other resources first, like the ones I listed above.
Students are responsible for understanding the academic integrity rules at Dartmouth. Explanations of integrity rules and principles can be found at http://www.dartmouth.edu/~uja/. Ignorance of the Academic Honor Principle will not be considered an excuse if a violation occurs. Beyond any penalties imposed as a consequence of an Academic Honor Principle investigation, any student who is found to have cheated or plagiarized on any assignment will receive a failing grade in the class. Details on citing sources are available here: https://writing-speech.dartmouth.edu/. Please see me immediately if you have any questions or concerns about academic integrity. Make sure to read this syllabus and any assignment guidelines carefully for academic integrity policies that are specific to this course.
Students with disabilities who may need disability-related academic classroom accommodations are encouraged to send me their documented accommodations ASAP. Students requiring disability-related academic adjustments and services must consult the Student Accessibility Services office (646-9900, student.accessibility.services@Dartmouth.edu).
Once SAS has authorized services, students must show the originally signed SAS Services and Consent Form and/or a letter on SAS letterhead to me. As a first step, if you have questions about whether you qualify to receive academic adjustments and services, you should contact the SAS office. All inquiries and discussions will remain confidential.
Some students may wish to take part in religious observances that occur during this academic term. If you have a religious observance that conflicts with your participation in the course, please meet with me before the end of the second week of the term to discuss appropriate accommodations.
The academic environment at Dartmouth is challenging, our terms are intensive, and classes are not the only demanding part of your life. There are a number of resources available to you on campus to support your wellness, including your undergraduate dean, Counseling Services, and the Student Wellness Center. I encourage you to use these resources to take care of yourself throughout the term, and to come speak to me if you experience difficulties. If you encounter financial challenges related to this class, please let me know.
Please read this entire section carefully so you understand the expectations of the course.
Participation – 5%
We all learn best when we are actively engaged. To encourage this, a small portion of your grade will be determined by participation. I recognize that students vary in their level of comfort with different types of participation. Participation will therefore include comments and questions during class as well as on Slack. As discussed above, I encourage you to answer questions and respond to comments from other students on Slack and to post questions and comments yourself as well. Engaging proactively and productively during group exercises in class will also count towards participation.
Quizzes – 10%
There will be a short, open-book quiz administered on Canvas before many classes about the content in the readings. Don’t let the term “quiz” spook you – you can think of these as “learning assessment questions.” They are designed to ensure you don’t fall behind. Quizzes are due before class time. They become available after the prior class ends and close 15 minutes before the next class begins. Once a quiz closes, it will not be opened up again. While these are open-book, you should complete these quizzes yourself with no assistance from your colleagues; you may not discuss them with other students prior to class. Correct answers are provided on Canvas after the quiz closes; there are no make-up quizzes. Each student’s lowest quiz grade will be dropped in final grade calculations. If there are other adjustments to quiz grades, it will apply to the entire class; individual waivers will not be granted and I cannot make an entire-class adjustment due to an individual circumstance.
Problem sets – 20%
Many weeks you will be assigned a problem set in R. For each problem set, you will create three files using RMarkdown (rmd, html, and pdf). Upload these files to Canvas before class begins on the due date. Make sure to not list your name anywhere in the documents or filenames, only your netID.
These are individual assignments that you should prepare yourself. You may work in groups of up to 4 people for help with the assignment, though you are not expected nor required to work in groups at all. These groups can be thought of similar to study groups: while you may work through the problems together, you should each contribute individually and produce your own code and write your answers to each question in your own words. You should never copy another person’s code/answers or send your code/answers to another person. This is a violation of the academic honesty policy. It is not a violation of the academic honesty policy to use R code from Stack Overflow or other websites and online forums, since that is a useful way to learn how to troubleshoot issues. But you should never directly copy written answers from the internet, such as someone else’s explanation or interpretation of the code or output. If you do use R code from the internet, please include a link to the site where you obtained the code in your submitted problem set. If you work with others to complete your problem set, please also list the names of the students you worked with. If you have any additional questions about what practices are okay, please ask!
Midterm exam – 15%
The midterm exam will cover the material covered in class up to that point. There will be both a paper and coding component. The exam is closed-book and students must work independently. A review session will be held in class prior to the exam.
Research project – 20%
You will be assigned to a group of about 4 students at the beginning of the term. You will work together to formulate a research question, design and execute a plan to study that research question, conduct statistical analyses to test your hypotheses, assess your design and analyses’ strengths and weaknesses, and draw out any implications of the research. You will write up and present your research at the end of term. The final project will consist of a written report of the results as well as a presentation on the research.
You will first turn in a 1-page research proposal in which you state your research question. The final written report and your R script file that replicates all analyses are due the day of presentations (before class). Failure to meet any of the deadlines (research proposal, team meetings, written report, presentation, etc.) will result in a reduced project grade. You will be evaluated on the quality of the proposal, data analysis, presentation, and written report. Do not worry about having statistically significant results! You will not be evaluated on whether your hypotheses were supported. More guidelines for the research project will be provided on Canvas/Slack and discussed in class.
Peer evaluations – 5%
To help ensure that each team member is actively contributing to the research project’s success, students will be asked to anonymously evaluate their teammates’ and their own contributions, effort, and performance. The end-of-term evaluations will be submitted once the research project is complete and the average of scores you receive from your teammates will be worth 5% of your research paper grade. You will receive a 0 for this portion of your grade if you do not submit evaluations for the rest of your team.
Final exam – 25%
A comprehensive closed-book final exam will be held on the day and time assigned by the college registrar (TBD). There will be both a paper and coding component. Students must work independently on the exam. We will review for the final exam on the last day of class.
This schedule may be modified at any time. Changes will be updated here and announced on Slack.
(Synchronous unless otherwise noted)
Readings & Assignments
(Due BEFORE class unless otherwise noted)
Recorded video to watch
(You are responsible to watch these on your own time. Recommended that you watch after the synchronous class meeting once we cover the stat concept, but you can watch before class if you want)
Overview of course: Why statistics?
- Obtain textbook!
- Take course survey on Canvas
- Download Slack App on your computer
- Join course Slack and Introduce yourself
- Read others’ posts on Slack and say hi
Measurement & Data
- Catch up on all of Tuesday’s assignments above
- Read: Agresti 1.1–1.4, 2.1–2.2
Due by 11:59 PM:
- Install R and RStudio (see the Statistical software section)
- Submit the Test RMarkdown homework on Canvas under “Assignments”
- If you have trouble with test RMarkdown assignment, post in Slack
Asynchronous (no class meeting)
Introduction to R
- Read: R Quick Tips Handout (after watching the tutorial videos) and keep this on hand for future reference!
- Watch: R Tutorial
- Watch: RMarkdown Tutorial
Sampling & Surveys
- Read: Agresti 2.3, 2.5 (+ quiz 1)
- Read: Nate Cohn, “A 2016 Review: Why Key State Polls Were Wrong About Trump.”
- Read: Nate Cohn, “What Went Wrong With Polling?”
(Problem set 1 assigned)
- Read: Agresti 3.1–3.7 (+ quiz 2)
- Problem set 1 due
(Research teams assigned)
- Watch: Descriptive Statistics in R
- Read: Agresti 4.1–4.7 (+ quiz 3)
- Read: Barakso, Sabet, Schaffner Ch 2
(Problem set 2 assigned)
- Read: Agresti 5.1–5.3 (+ quiz 4)
- Problem set 2 due
(Problem set 3 assigned)
- Read: Agresti 6.1–6.3 (+ quiz 5)
Due FRIDAY 4/23 6 PM:
- Research proposal due
- Watch: Hypothesis Testing in R
Inference with Significance Tests
- Read: Agresti 5.4, 6.4–6.5 (+ quiz 6)
- Problem set 3 due
- Study for midterm!
- Study for midterm!
Differences in Means and Proportions
- Read: Agresti 7.1–7.4 (+ quiz 7)
- Watch: Differences in Means and Proportions in R
Correlation & Linear Regression
- Read: Agresti 9.1–9.4 (+ quiz 8)
- edited: in place of problem set 4, submit draft survey questionnaire programmed in Qualtrics with a brief bullet point list of anything you changed from the research proposal
(Problem set 5 assigned)
- Watch: Correlation in R
- Read: Agresti 9.5–9.7 (+ quiz 9)
- Watch: Bivariate Regression in R
- Read: Agresti 10.1–10.5, 11.1.–11.3 (+ quiz 10)
- Problem set 5 due
(Problem set 6 assigned)
- Watch: Multivariate Regression in R
Causal Inference & Experiments
- Read: Costa and Wallace, 2021, “More Women Candidates” (+ quiz 11)
Due FRIDAY 5/21 1:00 PM EST:
- Problem set 6 due
Research project updates & peer review
- Preliminary slides & report due on Canvas before class
- Come prepared to give update about research project
Virtual presentation session! (& written reports due)
- Final slides due on Canvas before class
- Final written reports due before class
- Prep for virtual presentation session!
Final Exam Review
- Study for final exam
- Study for final exam