POL-UA 850
Introduction to Research Methods for Politics
Spring Semester 2023
Monday, Wednesday, 9:30 –10:45 AM ET
Prof. Anna Harvey
anna.harvey@nyu.edu
Virtual Office Hours: M 11:00 AM –12:00 PM ET (or by appointment)
Virtual Office Hours signup: https://www.wejoinin.com/sheets/ofrop
TA
Email
Mengfan Cheng
Alejandro López Peceño
Jimmy Graham
mc8199@nyu.edu
alp625@nyu.edu
jimmy.graham@nyu.edu
Overview
Can we identify racially discriminatory policing practices? Does access to health insurance save
lives? Does an increase in the minimum wage reduce employment? Do restrictions on abortion
negatively affect women’s economic outcomes?
In order to be able to answer these questions, it is useful to learn open-source programming for
data visualization and analysis, the basics of probability and statistics, as well as the principles of
causal inference.
This class will introduce you to these three topics. You will learn to use the open-source programming language R for data visualization and analysis, to conduct simple statistical tests, and
to apply the principles of experimental design to non-experimental data in order to make inferences
about causality. No prior programming or statistics experience is required.
Textbook
We will use the following required textbook, which is accessible through the NYU Bookstore, Amazon, and multiple other outlets. Note that digital versions of the textbook can be rented through
both Amazon and RedShelf.
Imai, Kosuke, and Elena Llaudet. 2022. Data Analysis for Social Science: A Friendly Introduction.
Princeton University Press (DSS).
1
Software
We will be using R, an open-source programming language, to conduct data visualization and
analysis. RStudio is an open-source graphical interface that is widely used to work with the R
language. The link to access our class RStudio environment on JupyterHub using your NYU netid
is below:
https://polua-850-spring.rcnyu.org
You can also install R and RStudio on your own computer using the links below. You can find
numerous resources for R on the internet. If you’d like to learn a little R before the semester begins,
you can take the free Introduction to R course in the DataCamp environment using the link below:
R download: https://www.r-project.org
RStudio download: https://www.rstudio.com
DataCamp: https://app.datacamp.com/learn/courses/free-introduction-to-r
The DSS textbook teaches R programming along with causal inference and introductory statistics.
The best way to learn R is to practice writing and executing code as you read the text. The R
scripts and datasets for DSS have been uploaded to our JupyterHub RStudio environment.
As you work through the text and the homeworks, the best place to ask questions is on CampusWire.You can join our class site with the code 7975 at this link:
https://campuswire.com/p/GCDE4369D
We encourage you not only to ask questions on CampusWire, but also to answer them. Engagement
on CampusWire will be factored into the course attendance/engagement grade (see below). Helping
other students with their programming questions is not only a nice thing to do, it earns you extra
engagement points.
Course Logistics and Requirements
This course consists of twice-weekly in-person lectures and a weekly in-person section. Because
of the possibility of illness-related absences, we will be livestreaming lectures on Zoom (links on
Brightspace) and also posting recorded lecture videos on Brightspace. Lecture and section slides
will be uploaded to Brightspace/JupyterHub. Please note that Zoom lectures are intended to provide a temporary solution to quarantine/isolation issues. Based on our experience, you will do
better in the class if you attend lectures in-person. We will be programming in both lecture and
sections; you will want to bring a laptop to both.
Grade: Your final grade will be based on a combination of:
• Homeworks (50%). There will be four homeworks. The first two homeworks are slightly
shorter and due within two weeks (both before the first midterm), and are each worth 10%
2
of the final grade. The second two homeworks are slightly longer and due within 3-4 weeks,
and are each worth 15% of the final grade. The homeworks will be focused on programming
for data analysis and will often involve real data. The homeworks *are not designed to be
completed the day before they are due.*
You may work on the homeworks with your classmates, though each student must write up
and submit their answers individually. If you choose to work with classmates, please write
the names of any individuals with whom you consulted at the top of your assignment. This
disclosure will protect you in the event of any concerns about academic integrity.
Submission instructions: All homeworks must be submitted remotely via Brightspace by 5
pm EST on the due date. You will be provided with an RMarkdown template on JupyterHub;
you will knit the Rmd template into a pdf or html for submission. If your Rmd document
does not knit because of a coding error, you may upload the Rmd document itself; points
will be deducted for the coding error or errors. Late assignments will be penalized 1/3 letter
grade per day late.
• Two midterm exams (30%): There will be two midterm exams, each worth 15% of the
course grade. Both midterms will be 24-hour take home exams. You may use the lecture
slides and recordings, your notes, and any assigned readings during the exam. However, you
may not consult with anyone on course material during these exams. Any clarifying questions
should be posted on Brightspace. The midterms will focus on content from Weeks 1-5 and
6-10, respectively. However, some topics are cumulative so earlier material may be relevant.
Submission instructions: Both midterms will be opened at at 9 am EST on Brightspace and
due at 9 am EST the next day. You will submit your midterms to Brightspace. As with
the problem sets, you will be provided with an RMarkdown template on JupyterHub; you
will knit the Rmd template into a pdf or html for submission. If your Rmd document does
not knit because of a coding error, you may upload the Rmd document itself; points will be
deducted for the coding error or errors. Late exams will be penalized.
• Final Exam (15%): The final will be 24-hour take home exam during the assigned exam
period. You may use the lecture slides and recordings, your notes, and any assigned readings
during the exam. However, you may not consult with anyone on course material during these
exams. Any clarifying questions should be directed to your TA by email. The final will be
cumulative with particular emphasis on the material from weeks 11-14.
Submission instructions: Pending guidance for final exams, the exam will be opened at at 9
am EST on Brightspace on the appointed date and due at 9 am EST the next day. You will
submit your final exams to Brightspace. As with the problem sets and midterms, you will
be provided with an RMarkdown template on JupyterHub; you will knit the Rmd template
into a pdf or html for submission. If your Rmd document does not knit because of a coding
error, you may upload the Rmd document itself; points will be deducted for the coding error
or errors. Late exams will be penalized.
• Attendance/Engagement (5%): Based on our experiences, we believe that you are more
likely to learn the material if you regularly attend both lectures and sections. We will be
taking attendance in both lecture and sections. Lecture attendance will be taken via 12 inlecture Brightspace quizzes with timed responses. Two nonresponses will be dropped for all
3
students; lecture attendance will be calculated from the remaining 10 quizzes. Section attendance will be taken by TAs. Engagement will be measured by participation on CampusWire.
Attendance/engagement will count for 5% of the course grade.
• Extra Credit (up to 2%): You will receive up to a maximum of 2% of the course grade
in extra credit by (virtually) attending up to 4 SSRC Centennial lectures (one each month
January - April), and writing a 1-page summary of the social/behavioral science research
covered in the lecture. Essay pdfs should be emailed to centennial lectures@ssrc.org within
1 week after lecture. There are 10 extra credit points available, 2.5 points for each essay pdf
submitted.
The final exam date is set by the registrar and cannot be moved. Once confirmed during lectures,
the midterm days and times are likewise firm. If you miss an exam due to some unavoidable
documented illness or other serious circumstance, we will not administer a make-up but will give
you a waiver from the exam.
Academic Integrity
The University is very clear that students’ work is expected to be their own, and that plagiarism
is not tolerated. The same rules apply here.
Homeworks:
You may consult and/or work with other students in this class, but any work you submit must be
your own. If you consult or work with another student, you must list their name at the top of your
submission. Do not copy another individual’s work or answers. Do not allow another individual to
copy your work or answers.
Take-home midterms and final exam:
You may use all resources from the course including texts, lecture materials, and section materials.
You may not communicate in any way with any individual about course material during any exam.
The only exception is for clarifying questions which should be directed to your TA. Do not copy
another individual’s work or answers. Do not allow another individual to copy your work or answers.
Schedule
The following is an anticipated schedule for course topics. We hope that this schedule is an accurate
plan of the classes that follow. Still, changes may need to be made and you will be informed of
them in advance: your responsibility as a student is to keep yourself informed of all such changes
and to be aware of exam and homework dates.
4
Lecture Number
1
2
3
4
5
6
7
8
Holiday
9
10
11
12
13
Holiday
Holiday
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Final
Lecture Date
1/23
1/25
1/30
2/1
2/6
2/8
2/13
2/15
2/20
2/22
2/27
3/1
3/6
3/8
3/13
3/15
3/20
3/22
3/27
3/29
4/3
4/5
4/10
4/12
4/17
4/19
4/24
4/26
5/1
5/3
5/8
TBD
Topic
Introduction 1
Introduction 2
Causal Effects 1
Causal Effects 2
Causal Effects 3
Population Characteristics 1
Population Characteristics 2
Population Characteristics 3
President’s Day
Population Characteristics 4
Midterm 1
Linear Regression 1
Linear Regression 2
Linear Regression 3
Spring Break
Spring Break
Linear Regression 4
Linear Regression 5
Linear Regression & Causality 1
Linear Regression & Causality 2
Midterm 2
Probability 1
Probability 2
Probability 3
Uncertainty 1
Uncertainty 2
Uncertainty 3
Uncertainty 4
Uncertainty 5
Summary
Review
Final Exam
5
Reading
DSS 1.1-1.4
DSS 1.5-1.9
DSS 2.1-2.2
DSS 2.3-2.4
DSS 2.5-2.6
DSS 3.1-3.2
DSS 3.3-3.4
DSS 3.5-3.6
Notes
HW 1 out
HW 1 due 2/10
HW 2 out
DSS 3.7-3.8
HW 2 due 2/24
DSS 4.1-4.2
DSS 4.3
DSS 4.4
HW 3 out
DSS
DSS
DSS
DSS
4.5
4.6-4.7
5.1-5.2
5.3-5.6
DSS
DSS
DSS
DSS
DSS
DSS
DSS
DSS
6.1-6.2
6.3-6.4
6.5-6.6
7.1
7.2
7.3
7.4
7.5
HW 3 due 3/31
HW 4 out
HW 4 due 5/5