Elena Llaudet

Elena Llaudet

Associate Professor of Political Science at Suffolk University

Co-author of Data Analysis for Social Science (with Kosuke Imai)

What's On This Page?

Resources for Data Analysis for Social Science

Quick Links

What's NOT On This Page?

  • The source files used to produce the syllabus, slides, and exercises
  • The datasets and solutions for the additional replication exercises

If you are an instructor using DSS as the main textbook in your course, you can request these materials from Princeton University Press (PUP) here.

Copyright and Attribution

These materials are based on our copyrighted textbook. If you use any of them, please provide proper attribution by citing both the original textbook and these resources:

  • Llaudet, Elena, and Kosuke Imai. Data Analysis for Social Science: A Friendly and Practical Introduction. Princeton University Press, 2022
  • Llaudet, Elena. "DSS Instructor Resources." ellaudet.github.io

For substantial use or adaptation, please contact the publisher and me.

COURSE OVERVIEW AND SYLLABUS

My course progresses through small, digestible exercises that students work through at least three times: first by following along with the analyses in the textbook on their own computer, then through the in-class exercises we complete together, and again in the weekly take-home problem sets. All three—textbook, in-class exercises, and problem sets—run in parallel, drawing on the same statistical concepts and code but applying them to different research questions and real-world datasets. This repeated, hands-on experience is key to building skills and shows students how to apply quantitative reasoning across a variety of contexts.

  • Logistics: My course meets for 24-26 sessions, each 75 minutes long. If you have fewer meetings, I recommend skipping the review sessions and/or one of the final lectures.
  • Content: It skips some of the more advanced topics in DSS because it is designed for undergraduates with no prior experience in coding or statistics, and minimal math background.
  • My syllabus is here (updated: 08/20/2026).
  • The in-class exercises are included in the lecture slides below, and my problem sets are identified among the additional replication exercises further down.
  • If you plan to use my student resources, I recommend directing students to the DSS Student Resources website, which includes all student-facing materials.
  • Instructors can request an exam copy of the book here.
  • All code and datasets used in the book are available in the DSS folder here. Those used in the additional replication exercises are provided only to instructors using DSS as the main textbook.
  • Questions? Feel free to email me at ellaudet [at] gmail [dot] com. Feedback? Please share it through this short survey (submissions can be anonymous).

LECTURE SLIDES (updated: 04/30/2026)

My lecture slides are meant to complement DSS, not replace it. They skip some of the more advanced topics covered in the book and do not repeat all the details found in it.

ADDITIONAL REPLICATION EXERCISES

These exercises draw on the same statistical concepts and code as the analyses in the book but use them to explore different research questions and real-world datasets. They give students repeated, hands-on experience—key to building skills—and show how to apply quantitative reasoning across a variety of contexts. They can be used as in-class exercises or as take-home assignments. (I have identified those I use as problem sets in my course.)

INTERACTIVE GRAPHS (work in progress)

Dynamic graphs that students can interact with in real time, directly in their browser, designed to build intuition about key concepts in statistics.

SELF-GRADED REVIEW EXERCISES (work in progress, updated: 08/21/2026)

Interactive R tutorials for students to check their understanding of the material on their own after completing the readings and performing the analyses explained in the book — they are meant to supplement, not replace, the book. Each includes multiple-choice questions and hands-on coding exercises with instant feedback. At the end, students can generate a progress report of their work. Eventually, there will be a tutorial for each chapter in the book. Check again soon! (To access them, run the code in the linked R script in RStudio. Watch the walkthrough video.)

VIDEOS AND ADDITIONAL READINGS (work in progress)