Spring 2021 - POL 315 D100

Intermediate Quantitative Methods (4)

Class Number: 4841

Delivery Method: Remote


  • Course Times + Location:

    We 12:30 PM – 1:20 PM

  • Prerequisites:

    POL 201 or permission of instructor.



Introduces intermediate quantitative methods and data analysis. Teaches students how to build statistical models and apply them to social and political research. Also covers the fundamentals of probability, sampling, and causal inference; students will learns how to conduct their own data-driven research. Quantitative.


Course Description

The goal of POL315 is to develop an intuitive and applied understanding of regression analysis. This course is intended for political science students who have completed POL201 and who now want to learn applied multiple regression analysis, with a focus on ordinary least squares (OLS) regression, for their own work and to understand the research of others.  The combination of quantitative skills and social scientific thinking are in high demand in a range of sectors, such as government, finance, the non-profit sector, advertising, marketing, and data science. This course will help you develop real skills that employers value.

Multiple regression is the workhorse of quantitative analysis in the social sciences. As such, it is important to understand why and how multiple regression works (or fails) under different conditions. This course begins with a review of causal inference and bivariate regression before covering OLS regression. We then cover dealing with non-linear relationships (interactions and polynomials), regression diagnostics (how to diagnose violations of our assumptions and what to do about it), and logistic regression. We will use the statistical software program R, although no previous experience with R is assumed or required.

If you have been thinking of strengthening your quantitative skills but have been intimidated, the pandemic may be a good opportunity to take this course. This version of POL315 has beed designed with the pandemic in mind: the course is somewhat self-paced and can be completed asynchronously. To acknowledge the stress that students are facing, this course has a lighter than normal workload with no mid-term or final exams, and with a short final project.

Course Format

Pre-recorded (asynchronous) lectures will be released on Mondays (“the Monday lecture”) and the class has the option of meeting on Wednesdays for a one-hour lab session to practice using the statistical software program, R (“the Wednesday lab”). During the recorded Monday lecture, the professor will offer an overview and explanation of the week’s topic.  During the Wednesday lab, the professor will offer a practical illustration of week’s topic in R.  During the Wednesday lab, students may follow along live and interrupt with questions.  The Wednesday labs will give students an opportunity to get hands-on practice doing statistical analyses with the help of the professor and will help the students prepare for their problem sets. Attendance at the Wednesday labs is completely voluntary. All labs will also be recorded and published on Canvas. Because all the materials will be available asynchronously, this course has no conflicts with other courses. In addition to office hours, the professor will be available to students to meet by appointment over Zoom.

Students may watch the recorded lectures when it is convenient for them, provided they watch the Monday lecture before the subsequent Wednesday lab. To help motivate students, every week there will be a mandatory quiz (“participation quiz”) based on the Monday lecture due anytime before the Wednesday lab. The quiz is open book and will be easy for students who have watched the lecture. With respect to homework, will be three problem sets due throughout the course. The problem sets will be based on the materials covered in the lectures and labs. Students will also have a relatively short (max 10 page) final project due at the end of the term. Students are also encouraged to submit an optional, short (max 1 page) research proposal prior to reading week. The optional research proposal is designed to encourage students to keep up with the course and to think ahead about their projects.  As motivation, students who submit the research proposal will receive a 1% bonus added to their final grade.


• Lectures: Released Monday at 2:30PM (pre-recorded, available online to watch asynchronously)
• Labs: Wednesday 12:30–1:20PM (option to attend synchronous labs, or watch recordings asynchronously)
• Office hours: Wednesdays 1:30-2:20PM, or by appointment


  • “Participation” (weekly quizzes based on lecture videos) 20%
  • Problem set 1 15%
  • Problem set 2 15%
  • Problem set 3 15%
  • Research Project Video Presentation (Optional (max 1 page) Research Project Proposal 1% bonus) 5%
  • Final Research Project (max 10 pages) 30%



Imai, Kosuke. 2017. Quantitative Social Science An Introduction. Princeton University Press.

Department Undergraduate Notes:

The Department of Political Science strictly enforces a policy on plagiarism.

Registrar Notes:


SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html


Teaching at SFU in spring 2021 will be conducted primarily through remote methods. There will be in-person course components in a few exceptional cases where this is fundamental to the educational goals of the course. Such course components will be clearly identified at registration, as will course components that will be “live” (synchronous) vs. at your own pace (asynchronous). Enrollment acknowledges that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes. To ensure you can access all course materials, we recommend you have access to a computer with a microphone and camera, and the internet. In some cases your instructor may use Zoom or other means requiring a camera and microphone to invigilate exams. If proctoring software will be used, this will be confirmed in the first week of class.

Students with hidden or visible disabilities who believe they may need class or exam accommodations, including in the current context of remote learning, are encouraged to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112).