POLI 300: Advanced Quantitative Methods in the Social Sciences

Spring Semester 2022

Tuesdays and Thursdays from 11:00am–12:20pm · Library 139

Professor Greg Thorson

Instructor: Professor Greg Thorson

Office: Hall of Letters 306

Phone: (909) 748-8636

Email: Greg_Thorson@redlands.edu

Office Hours: Wednesdays 2:30pm - 4:30pm and by appointment

Online Class Meeting Room (or Office Hours):
Zoom meeting room link is provided to enrolled students on Canvas.

Web Page:
http://facweb1.redlands.edu/fac/Greg_Thorson/

Public-facing version: removed Zoom meeting ID and cell note.

Course Description

Do you want to refresh your data analysis skills that you learned in previous classes? Are you interested in expanding your statistical skills even more? Do you like to manipulate datasets to test interesting social science hypotheses? Are you interested in understanding how policy researchers evaluate the effectiveness of public policy interventions? If you answer “yes” to any of these questions, this class may be perfect for you!

The purpose of this course is to expose students to the most commonly used advanced statistical techniques used in the social sciences, particularly those that are much more likely to determine whether a statistical association likely represents a causal relationship. Specifically, after reviewing some basic statistical concepts, we will learn about fixed effects models, difference-in-difference models, instrumental variables, regression discontinuity analysis, propensity score analysis, and synthetic models.

Each of these techniques will be explained both mathematically and conceptually. A strong command of math and calculus is NOT required for this class. Indeed, I welcome “math phobes”!

Like most professional social scientists, we will rely heavily on statistical software to do the mathematical computations. As a result, the course will also focus on mastering Stata, which is the most commonly used software in the social sciences. Students will learn both how to use Stata as well as how to interpret its output. We will also closely examine the assumptions of each of these techniques.

Courses covering these advanced quantitative methods are rare in undergraduate programs. Up until just the past few years, you would have to have been an advanced graduate student to learn the techniques covered in this course. This class is therefore designed to be useful for students considering graduate programs in the social sciences either nationally or internationally.

Course Prerequisite: Introductory course in statistics (preferably POLI 202)

Course Learning Outcomes

  • To review the foundations of quantitative analysis in the social sciences, including key concepts such as inference, hypothesis testing, and Ordinary Least Squares (OLS) regression,
  • To improve students’ command of the programming language used in Stata, the leadings statistical software used in the social sciences,
  • To introduce students to such advanced quantitative statistical models as fixed effects models, difference-in-difference models, instrumental variables, regression discontinuity analysis, and time series analysis,
  • To search for, identify, and review contemporary research findings related to key domestic and international public policy interventions aimed at solving complex social problems,
  • To write and orally present an effective policy brief that summarizes empirical research findings and draws attention to the strengths and limitations of the statistical methods used.

Required Texts, Hardware, and Software

Bailey, Michael A. 2021. Real Stats: Using Econometrics for Political Science and Public Policy (Second Edition). New York: Oxford University Press.

Paul, Richard and Linda Elder. 2019. The Miniature Guide to Critical Thinking: Concepts and Tools (Eighth Edition). Tomales, CA: Foundation for Critical Thinking.

Laptop or Desktop Computer (PC or Mac) that can run Stata 17 (see https://www.stata.com/support/faqs/windows/hardware-requirements/ for details).

Note: A version of Stata 17 may be available for you to download at no charge for course use.

Stata Version 17. Minimum Version: Stata 17/IC (6 months) - $48

Available online through https://www.stata.com/order/new/edu/gradplans/student-pricing/ (need student ID to get that pricing).

Want to know which version to buy? See Stata’s product comparison at http://www.stata.com/products/which-stata-is-right-for-me/

Stata/BE software with PDF documentation — $48/six months

Stata/SE software with PDF documentation — $125/six months

Stata/SE software with PDF documentation — $179/one year

Stata/SE software with PDF documentation — $425/perpetual

Recommendations are in bold. For most students, Stata/BE is more than enough. If you are considering a career as a policy researcher, are likely to go on to graduate school, or anticipate working with very large datasets, you might consider purchasing Stata/SE.

I also recommend that students purchase (and bring to class) a 64 GB USB flash drive. Please bring the flash drive to class by the second class session. Flash drives can be purchased at the University bookstore, as well as most retail electronics stores.

Other Resources to Help You Learn Stata

Want to learn more? Check out the following resources:

NameLink
Institute for Digital Research and Education (UCLA)http://www.ats.ucla.edu/stat/stata/
Social Science Computing Cooperative (University of Wisconsin)https://www.ssc.wisc.edu/sscc/pubs/stata_students1.htm
Germán Rodríguez (Princeton University)http://data.princeton.edu/stata/
Stata’s YouTube Channelhttps://www.youtube.com/user/statacorp
Stata’s NetCourses (Fees)http://www.stata.com/netcourse/

Grades

Final grades for the course will be based on your performance in the following areas:

  • Daily Quizzes/Participation: 30%
  • Assignments (Credit/No Credit): 10%
  • Current Research Studies: 10%
  • Take Home Final Exam/Policy Evaluation Brief: 50%
  • Extra Credit: Up to 5%

Course grades will be assigned using the following guidelines:

Course PercentGrade
93%-100%4.0
88%-93%3.7
83%-88%3.3
78%-83%3.0
74%-78%2.7
70%-74%2.3
66%-70%2.0
62%-66%1.7
58%-62%1.3
54%-57%0.7
Below 54%0.0

Daily Quizzes/Participation

Students will be graded on their timely presence in class as well as their participation in class discussion. Students are required to have read the assigned material before coming to class. Your participation grade will be based on your physical presence in class, the quantity and quality of your contributions to the class discussion, and the extent and quality of your preparation for class as measured by your participation in class and daily class quizzes.

Class attendance, as well as taking the quizzes, is required for completion of the course. We will take daily quizzes using Poll Everywhere. Please download the free Poll Everywhere software for your phone or tablet. While you can take the quiz on your computer from the Poll Everywhere website, students have experienced fewer glitches by using the various apps from their mobile devices. Please log in to the app using your @redlands.edu email address and choose PollEv.com/profthorson to join the presentation.

You will take daily quizzes that ask you about (a) the main points from the previous class discussion and (b) the major points in the new readings that will be discussed that day in class. These quizzes are designed to reward your attendance, your attention during class, and your preparation for class each day.

Current Research Studies

Students will periodically be assigned to identify and summarize (and critique) a current research study that uses one of the statistical methods covered in the course. The purpose of these studies is to help you connect the statistical techniques in the course to real-world policy research and analysis. You should be able to identify the research question, the data used, the method employed, and the key findings. You should also be able to critique the study’s assumptions and limitations.

Technology in the Classroom Policy

While I don’t formally ban the use of laptops, tablets, and other devices, research shows that screen-based note-taking can be distracting both to the user and to nearby students, and that traditional handwriting-based note-taking is often superior. Please use technology thoughtfully so that it supports rather than detracts from your learning and the learning environment.

Freedom of Expression

I am committed to the free expression of ideas in my classroom. Particular viewpoints should not be privileged simply because they are popular or supported by influential elites. All ideas and their corresponding assumptions must be defended by reason and evidence.

What does this mean for you? If you enroll in one of my classes, you may hear statements that you consider disagreeable, inappropriate, or offensive. All members of the class will be given very broad discretion to speak and write what they want. My classroom is not a “safe space,” nor is it designed to be a comfortable place.

Does this mean that you are allowed to say anything in my class? No. Among other restrictions on speech, you may not threaten or harass others in the class. You may also not use your freedom of expression to suppress the expression of the views of others in the class. Students who make offensive statements should also expect to be challenged by others who also enjoy their own freedom of expression.

It is my hope that our class can be as challenging and intellectually rigorous as possible.

Want to read more? The University of Chicago has produced their Report of the Committee on Freedom of Expression, which is widely regarded as the best statement of academic free speech in the United States. It is my sincere hope that, one day, we might adopt or adapt this position at the University of Redlands.

Accommodations for Students with Disabilities

I am happy to provide accommodations to students with disabilities. Please reach out to Disability Services as early as possible to set up these arrangements. Disability Services can be reached through the University’s student support offices.

Academic Honesty

Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; falsifying records; or fabricating or falsifying data.

You are responsible for both understanding and obeying both the letter and the spirit of academic honesty guidelines. Failure to obey these guidelines can result in both failure in the class and expulsion from the University.

Withdrawal/Incomplete

Students are responsible for voluntarily withdrawing from the class should they decide not to complete it. If your name appears on the registrar’s final grade sheet and I can find no work on which to base a grade, I must give you an “F.” I observe all University drop deadlines. Incomplete grades are given only under extraordinary circumstances.

Course Outline/Assignments

Please note this is a tentative schedule and may be adjusted at any time by the Professor:

Date Topic / Readings / Exercises
January 11

Syllabus and Introductions

January 13

The Rules of the Game/The Search for Causality

  • Bring Your USB Drive!
  • Paul and Elder, All
  • Bailey, Chapter 1
January 18

Good Data Practices

  • Bailey, Chapter 2
  • Exercises: Questions 1-2 (Due Next Class Period)
January 20

Bivariate Ordinary Least Squares (OLS) Regression

  • Bailey, Chapter 3.1 – 3.4
  • Exercises: Questions 1-2 (Due Next Class Period)
January 25

Bailey, Chapter 3.5 – 3.8

  • Exercises: Question 3 (Due Next Class Period)
January 27

Regression with Multiple Independent Variables

  • Bailey, Chapter 4
  • Exercises: Questions 1-2 (Due Next Class Period)
February 1

Regression Diagnostics

  • Bailey, Chapter 5
  • Exercises: Questions 1-2 (Due Next Class Period)
February 3

Nonlinear Relationships and Interaction Terms

  • Bailey, Chapter 6
  • Exercises: Questions 1-2 (Due Next Class Period)
February 8

Time Series and ARIMA Models

  • Bailey, Chapter 7.1 – 7.4
  • Exercises: Questions 1-2 (Due Next Class Period)
February 10

Time Series (continued)

  • Bailey, Chapter 7.5 – 7.7
  • Exercises: Questions 1-2 (Due Next Class Period)
February 15

Panel Data / Fixed Effects Models

  • Bailey, Chapter 8.1 – 8.3
  • YouTube Videos from Janux (Kevin Grier, Texas Tech):
  • Welcome to the Course
  • https://www.youtube.com/watch?v=MWFiwCBh6h0&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=1
  • The Problem of Causality in Observational Data
  • https://www.youtube.com/watch?v=1IKZZUTkh_w&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=2
February 17

Difference in Differences (Theory)

  • Difference in Differences, Part I
  • https://www.youtube.com/watch?v=cRrjU4kKyzY&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=3
  • Difference in Differences, Part II
  • https://www.youtube.com/playlist?list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK
February 22

Regression with Limited Dependent Variables

  • Bailey, Chapter 12.1 – 12.3
March 1 – 3

No Class - Spring Break

March 8

Bailey, Chapter 12.4 – 12.6

  • Exercises: Question 1 (modified) to be done together in class
March 10

No Class – Faculty Senate Meeting

  • (see recording for Stata Help)
March 15

Quasi-Experimental Methods: Fixed Effects Models

  • Bailey, Chapter 8.1 – 8.3
March 17

Two-Way Fixed Effects Models and Difference-in-Difference Models

  • Bailey, Chapter 8.4 – 8.5
March 22

Stata Practice – Fixed Effects and Difference-in-Difference Models

  • Exercises: Questions 1 and 4 to be done together in class (Due Next Class Period)
March 24

Instrumental Variables and Two-Stage Least Squares

  • Bailey, Chapter 9.1 – 9.6
  • No Exercises

Regression Discontinuity Analysis

  • Bailey, Chapter 11.1 – 11.2
  • YouTube Videos from Janux (Kevin Grier, Texas Tech): https://www.youtube.com/watch?v=DAeAAR5Lyzk&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=9
March 31

Bailey, Chapter 11.3 – 11.4

  • Exercises: Question 1 (Due Next Class Period)
April 5 - 7

Propensity Score Matching Analysis

  • Scott Cunningham, “Causal Inference: The Mixtape”. Chapter 5.
  • https://mixtape.scunning.com/matching-and-subclassification.html
  • Theory: YouTube Videos from Janux (Kevin Grier, Texas Tech)
  • Propensity Score Matching, Part I
  • https://www.youtube.com/watch?v=kgq0o1Ecqe4&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=5
  • Propensity Score Matching, Part II
  • https://www.youtube.com/watch?v=zWiLSB9NxfQ&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=6&t=49s
  • Propensity Score Matching, Part III
  • https://www.youtube.com/watch?v=ySQMsyY0DkM&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=7
  • Practice: YouTube Video by Chuck Huber, Director of Statistical Outreach, Stata
  • https://www.youtube.com/watch?v=hnyh1cUFiOE
  • Exercises: TBA (Due Next Class Period)
April 12 - 14

Synthetic Control

  • Scott Cunningham, “Causal Inference: The Mixtape”. Chapter 5.
  • https://mixtape.scunning.com/synthetic-control.html
  • YouTube Video from Janux (Kevin Grier, Texas Tech)
  • https://www.youtube.com/watch?v=1PQfeDT8zXM&list=PLTve54sz-eh_B8tR8ZGoPWgVKwiOMC0jK&index=8&t=4s
  • Exercises: TBA (Due Next Class Period)
April 14

Last Day of Class

  • Course Wrap-Up
  • Teaching Evaluations
  • Distribute Take Home Final Exam
  • Final Examination: Wednesday, April 20th from 9am – 11am
  • Students must upload their final exams to Moodle prior to the scheduled final exam period.