PSC 4375-01: Quantitative Social Science (Spring 2025)

Course Description

How does class size affect educational outcomes? What changes individuals' attitudes on gay marriage? What is the best way to predict election outcomes? What drives the onset of civil wars? Social scientists have tackled these questions (and numerous others!) using quantitative data and...quantitative methods. This course will equip students with essential analytical tools for conducting similar social science research, including causal inference, measurement techniques, and prediction models. Mastery of these methods is crucial for understanding complex social phenomena, enabling students to produce robust, reliable research and make informed decisions in their respective fields.

Beyond academia, leaders in government, private firms, and non-profits continue to invest heavily in data scientists to learn about their clients, users, and platforms. Data scientists employed at these respective institutions are essentially applied social scientists and utilize many of the same methods you will learn in this course.

So, you might be asking yourself, how is this course different from PSC 4175 "Intro to Data Science" or ECON 3137 "Intro to Econometrics" or STAT 1230 "Intro to Statistics"? Great question! There is certainly plenty of overlap. In the latter two courses, you might receive more attention to the mathematics that underpin the theory. In the former (taught by Prof. Weldzius), the focus is more on predictive models of data science. Similar to these courses, you will have some exposure to both theory and prediction; but throughout the course we will apply these methods to important research in the social sciences. If you learned one of these theories or methods in a previous course, use this time to perfect your understanding and seek new insights from its research applications.

Required Book: Imai, Kosuke and Nora Webb Williams. 2022. Quantitative Social Science: An Introduction in Tidyverse. Princeton University Press. [Supplementary Material].

Class time and location: M/W/F 12:50-13:40 in Bartley Hall 032.

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Expectations

In this course, you will be expected to

complete four problem sets,
complete ten weekly tutorials,
take two take-home, open-book exams, and
write one final data analysis .

Course Objectives

In this course, you will learn to:

Evaluate claims about causality
Summarize and visualize data
Be able to use linear regression to analyze data
Understand uncertainty in data analysis and how to quantify it
Use professional tools for data analysis such as R and RStudio

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Prerequisites

Most students will take PSC 1900, which provides a general overview of research methods in the social sciences, but probably little exposure to quantitative methods. Students with previous statistics exposure (e.g., AP Statistics or similar) or a solid background in high-school level math should also be prepared for the course.

We will assume a basic familiarity with high-school algebra and a working knowledge of computers. If you are unfamiliar with downloading and installing software programs on your Mac or PC, you may want to allocate additional time to make sure those aspects of the course go smoothly. In particular, I have developed Problem Set 0 to guide you through installing R and to give you a sense of the tools we will be using. Previous experience with statistical methods and computing (Stata, R, SAS, SPSS) is helpful, but not required. You can always get in touch with me for additional help on these issues.

No matter your background, you should be prepared to engage the class material on a regular, almost daily basis even beyond the time dedicated to assignments and exam review. Furthermore, you should feel comfortable with engaging in real-life data analysis using statistical software. I will guide you through that process, but it may be unfamiliar and therefore challenging. You should especially take this course if you plan to use quantitative data at all in a senior thesis project.

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Grading

You (the student) and I (the instructor) should care the most about what you learn, not what numerical/letter summary of that learning you get at the end of the semester. So I would love to not have grades at all, but unfortunately we humans are very good at procrastinating on our good intentions when there is no incentive not to. Thus, we have grades to help solve this commitment problem and to encourage you to put effort into learning the course material.

Here is how each portion of the course contributes to the overall grade:

Category	Percent of final grade
R Tutorials	10%
Four Problem Sets	40%
Exams	30%
Final Project	20%

You will use Blackboard for submission of the various assignments throughout the semester.

Bump-up policy: I reserve the right to “bump up” the grades of students who have made valuable contributions to the course in the lecture, tutorials, and/or discussion/CampusWire. This also applies to students who show tremendous progress and growth over the semester.

Letter grades are determined as per the standard Villanova grading system:

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

Blackboard Gradebook: although your grade for each assignment will appear on Blackboard, I will not use Blackboard to estimate your final grade. Blackboard will not calculate your final grade or estimate your final grade. I will calculate your final grade according to the percentages I highlight above, which you can also do using a simple Excel spreadsheet.

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Collaboration

You are effectively learning a new language in this course. You should not assume that you can do this all on your own. Thus, for every problem set cycle (there will be five cycles) I will split you into equally-sized groups. You will sit with your group for each class and will do any in-class activities with these classmates. You will be each other's primary contacts for issues relating to the problem set. I recommend you setup your own channel in Campuswire to chat with each other or exchange emails/phone numbers. I also recommend you arrange a time to meet before the problem set is due to check your answers with each other and troubleshoot any problems with R code. I will announce the groups and a seating chart at the start of each cycle (e.g., Monday 1/20 for Pset 1).

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Tutorials

I will assign short weekly tutorials to assess your knowledge of the material covered in the reading and lectures that week. You will complete these in RStudio. While you are expected to complete them on time, they will be graded based on completion not on how correct the answers are. They will be due Tuesdays by 11:59PM ET. Late submissions will be marked as incomplete; no exceptions.

NOTE: For tutorial 1-6 you should scroll down and find the "QSS Tidyverse Tutorial" version. For tutorials 7-10, you can use the "QSS Tutorial" version.

At the beginning of the term, these tutorials will focus on getting up to speed in R and over the course of the term, they will focus more on the theoretical aspects of data analysis.

Use these instructions for getting the tutorials setup in RStudio.

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Problem Sets

Only reading about data science is about as instructive as reading a lot about hammers or watching someone else wield a hammer. You need to get your hands on a hammer or two. Thus, in this course, you will have 4 problem sets to complete throughout the semester that will give you an opportunity to apply the statistical techniques you are learning. They will usually be focused on data analysis in general and will often involve a real dataset.

I encourage students to rely on peer working groups as they work on these problem sets, but each student will submit their own work individually.

The schedule for the problem sets will be:

Problem Set	Topic	Release Date	Due Date
1	Randomized Experiments	Fri (01/24) 12:00PM ET	Fri (01/31) 11:59PM ET
PSet 2	Summarizing Data	Fri (02/07) 12:00PM ET	Fri (02/14) 11:59PM ET ❤️
PSet 3	Regression	Fri (03/14) 12:00PM ET	Fri (03/28) 11:59PM ET
PSet 4	Inference	Fri (04/04) 12:00PM ET	Wed (04/16) 11:59PM ET

Late submissions: For each day your submission is late, your score will drop by one entire letter grade. After three days you will receive a zero. There will be no exceptions. You have one week to complete the problem set so do not wait until the last minute to begin.

Grace policy: When calculating the final problem set portion of the overall grade, I will drop the lowest of the four scores and use the remaining scores. Thus, if you have an emergency that forces you to miss one problem set, your grade will not be severely affected.

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Exams

There will be two take-home exams during the course. These exams will be similar to a problem set in format and in the sense that it will be open book and open internet, but you will not be allowed to collaborate with other students or be able to communicate with any humans about the exam. You will be given several days to complete the exam. I will provide more information about the exam as it approaches.

Exam	Release Date	Due Date
Exam 1	Fri (02/21) 12:00pm ET	Fri (02/28) 11:59pm ET
Exam 2	Fri (04/25) 12:00pm ET	Fri (04/25) 11:59pm ET

Late submissions: For each day your submission is late, your score will drop by one entire letter grade. After three days you will receive a zero. There will be no exceptions.

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Final Project

The final project for the course will be a data analysis project where students will find a dataset of interest, state an interesting research question about that data, and answer this question using that data. Students may work individually, or can work in groups of up to 3 students.

Milestone	Due Date
Proposal	Wed (03/12) by 11:59PM ET
Draft Analyses	Mon (04/28) by 11:59AM ET
In-class Presentations	04/28, 04/30
Final Report	Fri (05/09) by 11:59PM ET

Late submissions: For each day your submission is late, your score will drop by one entire letter grade. After three days you will receive a zero. There will be no exceptions.

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Flow of the Course

The course will follow a basic flow each week, with small differences if a homework or problem set is due or not.

Monday: Lecture 1 and discussion
Tuesday: Complete R tutorial
Wednesday: Lecture 2 and discussion
Friday: In-class practice with R. Note that Exams/Problem Sets are due on Fridays.

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Required Applications

R and RStudio

See pp. 8-9 in QSS for directions on how to download R and RStudio. More details follow:

Download and install the most recent version of R. There are versions available for the Windows, Mac, and Linux operating systems. On a Windows machine, you will want to install using the R-x.y.z-win.exe file where x.y.z is a version number. On a Mac, you will want to install using the R-x.y.z.pkg file that is notarized and signed.
With R installed, download and install RStudio. RStudio is a type of "integrated development environment" or IDE designed for R. It makes working with R considerably easier and is available for most platforms. It is also free.
Install the packages we will use throughout the semester. To do this, either type or copy and paste each of the following lines of code into the "Console" in RStudio (lower left panel by default). Make sure you do this separately for each line. If you are asked if you want to install any packages from source, type "no". Note that the symbols next to my_package are a less than sign < followed by a minus sign - with no space between them.

my_packages <- c("tidyverse", "usethis", "devtools", "learnr", "tinytex")
install.packages(my_packages, repos = "http://cran.rstudio.com")
remotes::install_github("kosukeimai/qss-package", build_vignettes = TRUE)

For some things in the course, we'll need produce PDFs from R and that requires something called LaTeX. If you've never heard of that, it's completely fine and you should just run the following two lines of R code:

install.packages('tinytex')
tinytex::install_tinytex()  # install TinyTeX

Blackboard

This is the course management software used at Villanova University to support course learning. It is clunky, not user-friendly, and is thankfully on its way out soon. For these reasons, I will only utilize Blackboard for you to submit your assignments and to see your grades/feedback. NB: Blackboard will show your grade for each assignment but will note calculate your final grade.

Campuswire

I have set up a Campuswire workspace for our use this semester to help us better communicate with each other. You will need to create an account and join our workspace by following this link. The secret PIN can be found on the first announcement on Blackboard. You are encouraged to adopt these Slack etiquette tips. Most likely, you will utilize a similar communication system at a future job, so use this time wisely as you adopt best practices.

Here are the channels you should see upon joining the Campuswire workspace and that we will most often utilize:

Class feed: A space to post questions and respond to other posts. When leaving a post on the class feed, be sure to choose the appropriate category (e.g., Problem Set 1).
#announcements: a space for all course announcements from Prof. Weldzius
#class-chat: a space for students to engage in conversation

GitHub

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 are connected to the appropriate folders, sub-folders, and files in this repository.

Microsoft Copilot

Villanova has an enterprise site license for Microsoft’s Copilot chat application, which is built off of Open A.I. Copilot is available to all faculty, staff, and students here.

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Course Policies

Attendance

Students are entitled to one excused absence for any reason that may contribute to their personal wellness. Students must advise the instructor by email before class of their intent to utilize a Personal Day as the reason for their absence. A Personal Day will not be approved retroactively. Students may, but are not required, to provide additional information regarding their absence. Additionally, a Personal Day may not:

be used immediately preceding or following a University holiday or break period;
be used on days when exams, presentations or other major assignments are scheduled.

A Personal Day does not grant an automatic extension for items due. Students remain responsible for all assignments, exams, presentations, etc. due on that date. It is in the instructor’s discretion to determine whether any extension is appropriate given individual circumstances.

Late Assignments

Every problem set and exam is assigned on a Friday and due on Blackboard by 11:59pm Villanova time on the following Friday. Problem sets and exams should be submitted via Blackboard. Late submissions will be penalized 1 full letter grade for each day late. After three days, problem sets and exams will no longer be accepted and will be scored 0.

Cell Phones, Laptops, Tablets, etc.

You are expected to bring your laptop to class in order to take notes and work through the problems along with Prof. Weldzius. You are asked to silence your cell phone / tablet / smart watch before class begins.

Academic Honor Code

All students are expected to uphold Villanova’s Academic Integrity Policy and Code. Any incident of academic dishonesty will be reported to the Dean of the College of Liberal Arts and Sciences for disciplinary action. You may view the University’s Academic Integrity Policy and Code for a detailed description.

If a student is found responsible for an academic integrity violation, which results in a grade penalty, they may not WX the course unless they are approved to WX for significant medical reasons. Students applying for a WX based on significant medical reasons, must submit documentation and their request for an exception will be considered.

Collaboration is the heart of data science, but your work on your assignments should be your own. Please be careful not to plagiarize. The above link is a very helpful guide to understanding plagiarism. In particular, while students are invited to work on problem sets together, collaboration is prohibited on the midterm and final exams.

AI (e.g., ChatGPT, Copilot) is an essential tool in the data scientist's toolkit, and acceptable resources for completing the assignments and learning concepts at a deeper level. However, there are specific ways in which its use is allowed:

Always include a log of your AI use (print the output or include a link if using ChatGPT)
You may use AI to help troubleshoot an error in your R code.
You may not use AI to solve the problem. This will result in an automatic zero and an academic integrity investigation. This is a zero tolerance policy.
If you use AI to hlep troubleshoot an error but do not include your log file or link to ChatGPT (ie, no citation is included) you will also receive a zero and an academic integrity investitation.

Office for Access & Disability Services (ADS) and Learning Support Services (LSS)

It is the policy of Villanova to make reasonable academic accommodations for qualified individuals with disabilities. All students who need accommodations should go to Clockwork for Students via myNOVA to complete the Online Intake or to send accommodation letters to professors. Go to the LSS website http://learningsupportservices.villanova.edu or the ADS website https://www1.villanova.edu/university/student-life/ods.html for registration guidelines and instructions. If you have any questions please contact LSS at 610-519-5176 or [email protected], or ADS at 610-519-3209 or [email protected].

Absences for Religious Holidays

Villanova University makes every reasonable effort to allow members of the community to observe their religious holidays, consistent with the University’s obligations, responsibilities, and policies. Students who expect to miss a class or assignment due to the observance of a religious holiday should discuss the matter with their professors as soon as possible, normally at least two weeks in advance. Absence from classes or examinations for religious reasons does not relieve students from responsibility for any part of the course work required during the absence. https://www1.villanova.edu/villanova/provost/resources/student/policies/religiousholidays.html.

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Office Hours

Wednesdays 2-4pm in SAC 257
You must make an appointment for Wednesday office hours here: https://calendly.com/weldzius/officehours

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Schedule

Week	Date	Topic	Assignment Due
0	01/13	Introduction to QSS	F: PSet 0
1	01/20	Causality: Randomized Experiments	T: QSS Tutorial 1
2	01/27	Causality: Observational Studies	T: QSS Tutorial 2 F: PSet 1
3	02/03	Measurement: Descriptive Statistics	T: QSS Tutorial 3
4	02/10	Measurement: Sampling & Bivariate Statistics	T: QSS Tutorial 4 F: PSet 2
5	02/17	Prediction: Elections & Regression	T: QSS Tutorial 5
6	02/24	Prediction: More Regression	F: Exam 1
7	03/03	Spring Break
8	03/10	Prediction: Interactions & Nonlinearities	T: QSS Tutorial 6 W: Final project proposal
9	03/17	Probability: Basics	T: QSS Tutorial 7
10	03/24	Probability: Random Variables & Large Samples	T: QSS Tutorial 8 F: PSet 3
11	03/31	Inference: Estimation	T: QSS Tutorial 9
12	04/07	Inference: Hypothesis Testing	T: QSS Tutorial 10
13	04/14	Inference: Uncertainty in Regression	W: PSet 4
14	04/21	Class Presentations
15	04/28	Class Presentations	M: Final project draft
16	05/05	Final project/Exam 2 due Friday (05/09) by 11:59PM ET	F: Exam 2 F: Final project

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Week 0

Topic: Course overview and introduction

Dates: 01/13 - 01/17

Readings:

M: Syllabus
W: QSS 1.1-1.4
F: R Markdown, 2.1-2.6

Assignments:

By Wednesday (01/15) before class: Setup R and RStudio. See instructions here. Initiate Campuswire account (see Required Applications above).
Problem Set 0 due Friday (01/17) by 11:59PM ET

Clas Materials:

Name		Name	Last commit message	Last commit date
Latest commit History 171 Commits
data		data
exams		exams
lectures		lectures
psets		psets
tutorials		tutorials
.DS_Store		.DS_Store
README.md		README.md

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PSC 4375-01: Quantitative Social Science (Spring 2025)

Table of Contents

Course Description

Expectations

Course Objectives

Prerequisites

Grading

Collaboration

Tutorials

Problem Sets

Exams

Final Project

Flow of the Course

Required Applications

R and RStudio

Blackboard

Campuswire

GitHub

Microsoft Copilot

Course Policies

Attendance

Late Assignments

Cell Phones, Laptops, Tablets, etc.

Academic Honor Code

Office for Access & Disability Services (ADS) and Learning Support Services (LSS)

Absences for Religious Holidays

Office Hours

Schedule

Week 0

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

Week 12

Week 13

Week 14

Week 15

Week 16

Helpful Resources

Acknowledgements

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