Elena Llaudet

Elena Llaudet

Associate Political Science Professor at Suffolk University

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

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

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 exercises 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 asking students to answer different research questions by analyzing other real-world datasets. This gives students repeated hands-on experience, which is key to building skills, and shows them how to apply quantitative reasoning across a variety of contexts.

LECTURE SLIDES (currently reviewing)

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 used in the exercises in the book, but ask students to answer different research questions by analyzing other real-world datasets, giving students the opportunity to apply the same statistical tools across a variety of contexts. They can be used as in-class exercises or as problem sets. (I have marked those I assign in my class.):

INTERACTIVE GRAPHS (work in progress)

To help students develop an intuition about some of the key concepts in statistics:

SELF-GRADED REVIEW EXERCISES (work in progress)

To enable students to check their understanding of the material on their own (run code in linked R script in RStudio):

VIDEOS (work in progress)

ADDITIONAL READINGS (work in progress)