SOCI 269
An Introduction to Quantitative Sociology—Culture and Power
Amherst College
Key Information
Instructor
Location
Time
Spring 2025—Tuesdays and Thursdays—2:30 to 3:50 PM
Office Hours
Fridays, 2:30-5:00 PM in Morgan Hall (Room 203 A) or during a Zoom Open Slot.
All meetings, even during office hours, must be scheduled in advance via Google Calendar.
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 2 from Karim (2024b)Structure
The course consists of four distinct modules:
Module I 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 simple coding exercises during lecture sessions. Collaboration will be encouraged.Module II will shift focus to the substantive realm—i.e., by spotlighting applied quantitative research published in many of sociology’s flagship journals. In Module II, I 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, you 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 your small group conversations.
Returning to statistical computing, Module III introduces the powerful
Pythonprogramming language. During the module, we will manipulate tabular data usingpandasandpolars, visualize data withseaborn, and build basic machine learning algorithms usingscikit-learn.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 or empirical scrutiny.
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 |
|---|---|---|---|
| Participation | Students are expected to actively participate in class discussions by raising their hands to share ideas, asking clarifying questions, assisting peers when needed, and providing constructive feedback during final presentations. | 10% | All Semester |
| Coding Assignment | Students are required to submit a short coding assignment in early March. For this assignment, they will clean a dataset in R, report basic descriptive statistics, and create simple data visualizations. Students must also include their script file (i.e., a .R document) as part of their submission. Additional assignment instructions can be accessed here. |
10% | Monday, March 10th at 8:00 PM. |
| Midterm Assignment | For their midterm assignment, students must—either individually or in groups of 2-3—submit a relatively complex data visualization; an annotated script file or Quarto/RMarkdown document featuring their underlying code; and a 5–10-page reflection memo (double-spaced) where they interpret their results and establish connections between their visualization and recent social scientific scholarship. Additional assignment instructions can be found here. | 30% | Friday, April 4th at 8:00 PM. |
| Final Presentations | In Module IV of the class, students will deliver a 10–15-minute presentation based on, or informed by, their term paper. A rubric detailing my basic expectations will be included in this syllabus by early April. | 15% | During Module IV |
| Term Paper | Drawing on the applied examples featured in Module II, students must submit a term paper on a topic related to (i) race, ethnicity and nation; (ii) gender and sexuality; or (iii) culture. To earn an A, students must also submit a companion data visualization using a truncated version of the General Social Survey, which will be made available on Moodle. Students are free to create this visualization in either R or Python. Additional assignment instructions will be posted online by early April. |
35% | Friday, May 9th at 8:00 PM. |
Guidelines for Key Deliverables
Guidelines for the three key deliverables—i.e., the midterm assignment; the final presentation; and the term paper—will be gradually rolled out (or uploaded online) as deadlines come into focus.
Coding Assignment
You can access the assignment instructions by clicking here or expanding the tab below.
“Midterm” Assignment
To access assignment instructions, click here or expand the tab below.
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 48 hours. 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 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.
Accessibility and Accommodations
If you require accommodations, please contact Student Accessibility Services as soon as possible and submit an application through the new AIM Portal. More generally, if you have any suggestions about how this class can be more accessible and inclusive, please let me know via e-mail or during office hours.
Weekly Schedule
As noted, non-textbook readings can be accessed via the eReserves page on our course website.
Readings underlined below are recommended but optional.
Module I: An Introduction to
Week 1: Introduction to the Course and Setting Up
January 28th & January 30th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham et al. 2023)
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: Cleaning and Data Analysis in
February 4th & February 6th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham et al. 2023)
Week 3: Cleaning and Data Analysis in II
February 11th & February 13th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham et al. 2023)
Week 4: Data Visualization with ggplot2
February 18th & February 20th
Data Visualization: A Practical Introduction
(Healy 2019)
ggplot2: Elegant Graphics for Data Analysis
(Wickham et al. 2025)
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham et al. 2023)
Week 5: Data Visualization with ggplot2 II
February 25th & February 27th
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
(Wickham et al. 2023)
Data Visualization: A Practical Introduction
(Healy 2019)
ggplot2: Elegant Graphics for Data Analysis
(Wickham et al. 2025)
Module II: Applied Examples
Week 6: Race, Ethnicity and Nation
March 4th & March 6th
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)
The Organization of Ethnocultural Attachments Among Second-Generation Germans
(Karim 2024b)
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)
The Curious Case of Black ‘Conservatives’: Assessing the Validity of the Liberal-Conservative Scale among Black Americans
(Jefferson 2024)
Administrative Records Mask Racially Biased Policing
(Knox, Lowe, and Mummolo 2020)
Your coding assignments are due by 8:00 PM on Monday, March 10th.
Week 7: Gender and Sexuality
March 11th & March 13th
School, Studying, and Smarts: Gender Stereotypes and Education Across 80 Years
of American Print Media
(Boutyline, Arseniev-Koehler, and Cornell 2023)
Abortion and Women’s Future Socioeconomic Attainment
(Everett and Taylor 2024)
Has There Been a Transgender Tipping Point? Gender Identification Differences in U.S. Cohorts Born between 1935 and 2001
(Lagos 2022)
Money, Birth, Gender: Explaining Unequal Earnings Trajectories Following Parenthood
(Machado and Jaspers 2023)
Intersecting the Academic Gender Gap: The Education of Lesbian, Gay, and Bisexual America
(Mittleman 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 8: Spring Break—
Week 9: Culture
March 25th & March 27th
Pluralistic Collapse: The ‘Oil Spill’ Model of Mass Opinion Polarization
(DellaPosta 2020)
Mapping Shared Understandings Using Relational Class Analysis: The Case of the Cultural Omnivore Reexamined
(Goldberg 2011)
Islam and the Transmission of Cultural Identity in Four European Countries
(Karim 2024a)
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)
Module III: An Introduction to Python
Week 10: Cleaning and Data Analysis in Python
April 1st & 3rd
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 midterm assignments are due by 8:00 PM on Friday, April 4th.
Week 11: A Very Gentle Introduction to Machine Learning in Python
April 8th
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
(Loon 2022)
Week 12: Data Visualization with seaborn
April 15th & 17th
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter
(McKinney 2022)
Module IV: The Final Stretch
Week 13: Final Presentations I
April 24th
Week 14—
Week 15: Final Presentations II
May 6th
Your term papers are due by 8:00 PM on Friday, May 9th.
Slides should be up within 24 hours of a class session.
Module I: An Introduction to
Week 1
Week 2
Week 3
Week 4
Week 5
Module II: Applied Examples
Week 6
Week 7
Week 9
Recommended Readings
Just to drive the point home: underlined readings are recommended but optional.