Elena Llaudet

ELENA LLAUDET

Associate Professor of Political Science at Suffolk University

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

HOME | PUBLICATIONS | TEACHING | DSS INSTRUCTOR RESOURCES

What's On This Page?

  • My syllabus
  • My lecture slides
  • The code and real-world datasets used in the exercises in the book
  • The questions asked in the additional replication-style exercises we provide instructors using DSS, which can be used as in-class exercises or as take-home problem sets
  • Links to interactive graphs to help students develop an intuition about some of the trickier concepts in statistics
  • Other resources under development such as review exercises, videos, and additional readings.

What's NOT On This Page?

  • The files necessary to produce and change my syllabus, my lecture slides, and the additional replication-style exercises
  • The datasets analyzed in the additional replication-style exercises
  • The solutions to given exercises.

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

Other Resources:

  • DSS Student Resources: This website hosts all the materials I have created specifically for student use, including interactive graphs and review exercises. It excludes instructor-only materials available on this site, such as my syllabus, lecture slides, and additional replication-style exercises.
  • The first chapter is available for free here. You can find the book in Amazon here, request an exam copy here, and provide feedback directly to the authors here.
  • The GitHub repository with the practice exercises (learnr tutorials) I have created is here. An the one with the interactive graphs (shiny apps) I have created is here.

INSTRUCTOR RESOURCES for DATA ANALYSIS FOR SOCIAL SCIENCE (DSS)

MY COURSE

My course progresses through bite-sized exercises, which students have a chance to practice at least three times: once with the textbook, once with the in-class exercises, and once with the take-home weekly problem sets. All three–textbook, class, and problem sets–move in parallel, asking similar questions, but using different real-world datasets so that students get to see the same material in different contexts.

Although also provided below chapter-by-chapter, all the code and all the real-world datasets used in the exercises in the book are in a folder named DSS here. We recommend downloading this folder, unzipping it, and saving it directly on your Desktop, which is where the code used throughout the book assumes the DSS folder is located. (Datasets and code are also available in this GitHub repository.)

IMPORTANT NOTES

  • There is a lot more material in DSS than what my lecture slides cover. My course skips some of the more advanced-level material in DSS because it is meant for undergraduate students with no prior knowledge of coding or statistics and only minimal knowledge of math.
  • My lecture slides are meant to complement DSS, not be a substitute. They assume students come to class having done the readings and having followed along with the exercises in the book on their own computer. In class, we go over a different replication-style exercise (asking similar questions but analyzing different data) and my slides do not always repeat all the explanations and details given in the book.
  • Any errors found in the these resources are my own. If you find any, I would really appreciate it if you could let me know by sending me an email at ellaudet@gmail.com. (Updated: 11/02/2023)

CHAPTER 1: INTRODUCTION

In chapter 1, we start from the very beginning by installing and familiarizing ourselves with the two programs we use―R and RStudio―and by laying the groundwork for forthcoming analyses. 

CHAPTER 2: ESTIMATING CAUSAL EFFECTS WITH RANDOMIZED EXPERIMENTS

In chapter 2, we learn what causal effects are and how to estimate them using randomized experiments. We analyze data from Project STAR to answer: What is the effect of small classes on student performance?

CHAPTER 3: INFERRING POPULATION CHARACTERISTICS VIA SURVEY RESEARCH

In chapter 3, we learn about surveys and how to visualize and summarize the distribution of single variables as well as the relationship between two variables. We analyze data on the 2016 British referendum to answer: Who Supported Brexit?

CHAPTER 4: PREDICTING OUTCOMES USING LINEAR REGRESSION

In chapter 4, we learn how to predict outcomes using simple linear regression models. We analyze data from 170 countries to predict GDP growth using night-time light emissions as measured from space.

CHAPTER 5: ESTIMATING CAUSAL EFFECTS WITH OBSERVATIONAL DATA

In chapter 5, we learn how to estimate causal effects using observational data. We analyze survey and electoral data to answer: What was the effect of Russian TV propaganda on the 2014 Ukrainian elections?

CHAPTER 6: PROBABILITY

In chapter 6, we cover basic probability. We learn about random variables and their distributions, the distinction between population parameters and sample statistics, and the two large sample theorems that enable us to measure statistical uncertainty.

CHAPTER 7: QUANTIFYING UNCERTAINTY

In chapter 7, we learn how to quantify the uncertainty in our empirical findings in order to draw conclusions at the population level. We complete some of the analyses we started in chapters 2 through 5.