Soci–269
AN INTRODUCTION TO QUANTITATIVE SOCIOLOGY—CULTURE & POWER
Course Description
How do sociologists define, model, and visualize social phenomena using quantitative tools and statistical software? This seminar will provide a technical, theoretical and practical overview. During the semester, students will learn how to use and Python to clean, analyze and visualize data that are suitable for sociological analysis. At the same time, the course will interrogate how social inequality can be masked—and deeply pernicious ideas can be reproduced—if quantitative data analyses are not informed by, or sensitized to, social theory and the hierarchies of power and privilege that structure the social world. To this end, we will engage with recent work in cultural sociology that draws attention to variation within and across social groups (defined in terms of race, gender, class and so on) to understand how social inequalities emerge and endure. Throughout the course, we will scrutinize policy-relevant social issues while discussing topics like race, ethnicity, religion, class, gender and sexuality.
Prior knowledge of statistics or programming is not required but may be an asset.
Figure 1 from Soehl and Karim (2021)Structure
The course consists of four distinct modules:
| Module I will spotlight applied quantitative research published in many of sociology’s flagship journals. Class sessions will begin with a basic lecture informed by the week’s readings. Then, I will toss the baton over to all of you. Working in small groups, students will respond to the questions or prompts I provide. Each synchronous session will conclude with a plenary discussion, where we will explore the themes that emerged during small group conversations. |
Module II will provide a comprehensive introduction to the programming language for statistical computing and visualization, with significant attention paid to data visualization using ggplot2. Classes will largely be hands-on and interactive. That is, students will regularly work on coding exercises during lecture sessions. Collaboration will be encouraged. |
Module III introduces the powerful Python programming language. During the module, students will manipulate tabular data using pandas and polars, visualize data with seaborn, and build basic machine learning algorithms using scikit-learn. Once again, class sessions will feature coding exercises and ample opportunities for collaboration. |
| Module IV will feature a series of in-class presentations related to your term papers. |
Readings
| Data Visualization: A Practical Introduction (Healy 2019) |
| Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter (McKinney 2022) |
| R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Wickham, Çetinkaya-Rundel, and Grolemund 2023) |
| ggplot2: Elegant Graphics for Data Analysis (Wickham, Navarro, and Pedersen 2025) |
Supplemental readings are available through the mystifying power of Moodle. As you plan for the semester, it may be useful to bookmark the eReserves page on our course website. New readings may be introduced as the world around us evolves, whether via the incremental march of science or in response to social, economic and political shocks that warrant further reflection.
Recommended Readings
As a forewarning: readings underlined in the Weekly Schedule section are recommended but broadly optional.
Evaluations
A Bird’s Eye View
| Task | Description | Weight | Deadline or Evaluative Time Horizon |
|---|---|---|---|
| Response Memos | During Module I, students must engage with, and respond to, questions posed on our Moodle Discussion Board. Responses must be between 250–400 words, or a penalty will be applied. Students are free to “skip” one week (i.e., by not submitting a memo) without incurring a penalty. | 10% | 8:00 PM on Mondays. Evaluated from Weeks 2 to 5. |
| Participation | Students must actively participate in class discussions by raising their hand to share their thoughts, assisting peers when needed, or meaningfully contributing to small group conversations. | 10% | Evaluated during class sessions throughout the term. |
| Coding Assignment in | Students must—either individually or in groups of two—submit their first coding assignment in early November. For this assignment, they will use to clean data from the General Social Survey; report descriptive statistics; create data visualizations using ggplot2; and provide a 5-10-page interpretation of their results (double-spaced, 12-point font). Students must also include their script file (i.e., a .R document) as part of their submission. Additional assignment instructions can be found here. |
20% | Wednesday, November 5th at 8:00 PM. |
| Coding Assignment in Python |
Students must—either individually or in groups of two—submit their second coding assignment in late November. For this assignment, they will use Python to clean data from the American National Election Studies; report descriptives; create data visualizations using seaborn; and provide a 5-10-page summary of their results (double-spaced, 12-point font). Students must also include their code (e.g., a .ipynb document) as part of their submission. Additional instructions can be found here. |
20% | Friday, November 21st at 8:00 PM. |
| Final Paper Presentation |
Students will deliver a 7–10-minute presentation based on, or informed by, their term paper. A rubric detailing my basic expectations will be posted later in the term. | 10% | During Module IV of our class. |
| Final Paper | Drawing on the applied examples featured in Module I, students must submit a 10-15-page term paper (double-spaced, 12-point-font) on a topic broadly related to (i) gender and sexuality; (ii) race, ethnicity, and nation; (iii) class and social stratification; or (iv) culture. To earn an A, students must also submit companion data visualizations using data from the General Social Survey or the American National Election Studies. Students are free to create these visualizations in either or Python . A rubric detailing my basic expectations will be uploaded later in the term. |
30% | Wednesday, December 17th at 8:00 PM. |
Guidelines for key deliverables will be gradually rolled out (or uploaded online) as deadlines come into focus.
Guidelines for Coding Assignment in
You can access guidelines for your first assignment by clicking here or hitting the button below.
Instructions (Click to Expand/Close)
Guidelines for Coding Assignment in Python
You can access guidelines for your second assignment by clicking here or hitting the button below.
Instructions (Click to Expand/Close)
Norms, Rules & Regulations
Please review the Amherst College Honor Code, which can be accessed in its entirety here.
Violations of the Honor Code will be promptly reported to the Dean of Students. As Section 1.1 of the Honor Code indicates, plagiarism is a serious offense. In most cases, students who plagiarize the work of others will fail this class and may face additional disciplinary penalties. Moreover, as detailed in Sections 1.2 to 1.4 of the Honor Code, students must respect others in the classroom, including those whose views deviate from their own. Failure to do so will prompt disciplinary action.
There is no reason to pretend like generative artificial intelligence (GAI) does not exist in the world out there. These systems have arrived, and they may revolutionize how higher education “works.” With this in mind, you are free to use ChatGPT and its analogues for class assignments—but you have to cite the GAI you are using. Failure to do so amounts to plagiarism.
To reiterate:
If you use a GAI tool (like ChatGPT) and do not cite it, it is a form of plagiarism.
You are expected to attend each and every class. If you do not, you will lose points for participation. That said, I am aware that you are all human beings whose lives are often fraught with uncertainty. If something comes up, please let me know and I will do my best to be as accommodating as possible. Extended absences may, however, require additional documentation (e.g., note from a physician).
Provisionally, I have decided to allow students to use laptops and tablets in class. This is, however, highly conditional. If I observe students using their electronic devices for non-academic pursuits (e.g., shopping, consuming social media and so on), I will institute a sweeping ban on electronics.
Do not be the one to contravene our social contract.
On weekdays and non-holidays, I will respond to e-mails within two days. If I fail to meet this standard, please send me a follow-up message with a gentle reminder. On weekends1 and breaks, I will not respond to e-mails unless you have an emergency. If you do, please remember to include EMERGENCY in the subject line.
Assignments must be submitted on time. A late submission will result in a penalty of 5% for each day beyond the deadline.2 However, as noted, I am well aware that life can present unexpected challenges. If you anticipate missing a deadline or have an emergency, please let me know soon as you can. Extensions may be granted on a case-by-case basis.
A Note On Office Hours
I will hold my in-person office hours on Fridays from 9:00 AM to 11:00 AM in Morgan Hall (Room 306), although students can also schedule meetings during an Open Meeting Slot.3 All meetings—even during office hours—must be scheduled in advance via Google Calendar. To reiterate:
All meetings, even during office hours, must be scheduled in advance via Google Calendar.
Need Directions? (Click to Expand and/or Close)
Accessibility and Accommodations
If you require accommodations, please contact Student Accessibility Services as soon as possible and submit an application through the AIM Portal. More generally, if you have any suggestions about how this class can be more accessible and inclusive, please let me know.
Weekly Schedule
As noted, all readings can be accessed via the eReserves page on our course website.
Readings underlined below are recommended but optional.
Module I: Quantitative Social Research
Week 1: Introduction to the Class
September 3rd
No required readings.
Transcending General Linear Reality
(Abbott 1988)
Sequence Analysis: New Methods for Old Ideas
(Abbott 1995)
Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable
(Elwert and Winship 2014)
What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
(Lundberg, Johnson, and Stewart 2021)
Inequality without Groups: Contemporary Theories of Categories, Intersectional Typicality, and the Disaggregation of Difference
(Monk 2022)
Computational Grounded Theory: A Methodological Framework
(Nelson 2020)
Week 2: Gender and Sexuality
September 8th & September 10th
School, Studying, and Smarts: Gender Stereotypes and Education Across 80 Years
of American Print Media
(Boutyline, Arseniev-Koehler, and Cornell 2023)
Money, Birth, Gender: Explaining Unequal Earnings Trajectories Following Parenthood
(Machado and Jaspers 2023)
Abortion and Women’s Future Socioeconomic Attainment
(Everett and Taylor 2024)
Intersecting the Academic Gender Gap: The Education of Lesbian, Gay, and Bisexual America
(Mittleman 2022)
Has There Been a Transgender Tipping Point? Gender Identification Differences in U.S. Cohorts Born between 1935 and 2001
(Lagos 2022)
Sexual Orientation Identity Mobility in the United Kingdom: A Research Note
(Hu and Denier 2023)
Marriage, Cohabitation, and Institutional Context: Household Specialization among Same-Sex and Different-Sex Couples
(Yang 2025)
Week 3: Race, Ethnicity, and Nation
September 15th & September 17th
Contraction as a Response to Group Threat: Demographic Decline and Whites’ Classification of People Who Are Ambiguously White
(Abascal 2020)
The Partisan Sorting of “America”: How Nationalist Cleavages Shaped the 2016
U.S. Presidential Election
(Bonikowski, Feinstein, and Bock 2021)
The Politics of Police
(Donahue 2023)
Double Jeopardy: Teacher Biases, Racialized Organizations, and the Production of Racial/Ethnic Disparities in School Discipline
(Owens 2022)
Separate from Class? Toward a Theory of Race as Resource Signal
(Torres 2024)
Reclaiming the Past to Transcend the Present: Nostalgic Appeals in U.S. Presidential Elections
(Bonikowski and Stuhler 2022)
Slavery’s Carceral Legacy
(Clegg 2025)
From the Block to the Beat: How Violence in Officers’ Neighborhoods Influences Racially Biased Policing
(Donahue and Torrats-Espinosa 2025)
The Curious Case of Black ‘Conservatives’: Assessing the Validity of the Liberal-Conservative Scale among Black Americans
(Jefferson 2024)
The Organization of Ethnocultural Attachments Among Second-Generation Germans
(Karim 2024a)
Administrative Records Mask Racially Biased Policing
(Knox, Lowe, and Mummolo 2020)
Week 5: Culture
September 29th & October 1st
How Does Culture Matter for Attainment, and How Would We Know If It Did?
(Brady, Luft, and Zuckerman Sivan 2025)
Pluralistic Collapse: The ‘Oil Spill’ Model of Mass Opinion Polarization
(DellaPosta 2020)
Islam and the Transmission of Cultural Identity in Four European Countries
(Karim 2024b)
Life-Course Transitions and Political Orientations
(Keskintürk 2024)
Change in Personal Culture over the Life Course
(Lersch 2023)
Theoretical Foundations and Limits of Word Embeddings: What Types of Meaning Can
They Capture?
(Arseniev-Koehler 2024)
Cultural Schemas: What They Are, How to Find Them, and What to Do Once You’ve Caught One
(Boutyline and Soter 2021)
Party, Race, and Neutrality: Investigating the Interdependence of Attitudes toward Social Groups
(Brensinger and Sotoudeh 2022)
Mapping Shared Understandings Using Relational Class Analysis: The Case of the Cultural Omnivore Reexamined
(Goldberg 2011)
Module II: An Introduction to
Week 6: Data Wrangling in
October 6th & October 8th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham, Çetinkaya-Rundel, and Grolemund 2023)
Week 7: Data Wrangling in (continued)
October 15th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham, Çetinkaya-Rundel, and Grolemund 2023)
Week 8: Data Visualization with ggplot2
October 20th & October 22nd
Data Visualization: A Practical Introduction
(Healy 2019)
ggplot2: Elegant Graphics for Data Analysis
(Wickham, Navarro, and Pedersen 2025)
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham, Çetinkaya-Rundel, and Grolemund 2023)
Week 9: Data Visualization with ggplot2 (continued)
October 27th & October 29th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham, Çetinkaya-Rundel, and Grolemund 2023)
Module III: An Introduction to Python
Week 10: Wrangling Data in Python
November 3rd & November 5th
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter
(McKinney 2022)
- Chapter 1: Preliminaries
- Chapter 5: Getting Started with
pandas - Chapter 6: Data Loading, Storage, and File Formats
- Chapter 7: Data Cleaning and Preparation
Your first coding assignment is due by 8:00 PM on Wednesday, November 5th.
Week 11: An Introduction to seaborn
November 10th & November 12th
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter
(McKinney 2022)
Week 12: Machine Learning in Python and an Open Work Session
November 17th & November 19th
No required readings.
Researcher Reasoning Meets Computational Capacity: Machine Learning for Social Science
(Lundberg, Brand, and Jeon 2022)
Machine Learning for Sociology
(Molina and Garip 2019)
Leveraging the Alignment between Machine Learning and Intersectionality: Using Word Embeddings to Measure Intersectional Experiences of the Nineteenth Century U.S. South
(Nelson 2021)
Predictability Hypotheses: A Meta-Theoretical and Methodological Introduction
(van Loon 2022)
Your second coding assignment is due by 8:00 PM on Friday, November 21st.
Week 13: Thanksgiving Break
Module IV: Final Presentations
Week 14: First Week of Presentations
December 1st & December 3rd
Week 15: Second Week of Presentations
December 8th & December 10th
Term papers are due by 8:00 PM on Wednesday, December 17th.
Should be available within 24 hours of a class session.
Module I: Quantitative Social Research
Week 1
Week 2
Week 3
Week 4
Week 5
Module II: An Introduction to
Week 6
Week 7
Week 8
Week 9
Module III: An Introduction to Python
Week 10
Recommended Readings
Just to drive the point home: underlined readings are recommended but optional.