Elena Llaudet

Elena Llaudet

Associate Political Science Professor at Suffolk University

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

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What's On This Page?

Instructor and student resources for Data Analysis for Social Science (DSS):

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.

OVERVIEW OF MY COURSE

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.

LECTURE SLIDES (recently revised; source files provided by PUP updated on 08/20/2025)

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)

Interactive R tutorials for students to check their understanding of the material on their own after completing the readings. Each includes 50-60 questions, combining multiple choice and hands-on coding exercises. Students receive instant feedback and can generate reports showing proportion completed. Eventually, there will be a tutorial for each chapter in the book. Check again soon! (To access, run code in linked R script in RStudio.)

VIDEOS AND ADDITIONAL READINGS (work in progress)