Syllabus
Description
This course explores quantitative research designs to answer questions about public opinion and policy in academia, government, and industry. We will examine how to conduct surveys to understand variation in public opinion or attitudes toward several subjects across the world. We will also examine empirical strategies to generate credible evidence to inform policy and decision-making in different contexts.
The main learning objective of this course is to develop standards to think about the appropriate research design before conducting a study. This is useful to those working with quantitative data either directly or indirectly. We will work towards that goal through a combination of reading, discussion, and hands-on practice.
Students will have the opportunity to practice working with statistical software to evaluate and compare the statistical properties of alternative research designs. Students will also get the chance to practice discussing and communicating research design choices to a general or expert audience through speech and writing.
This course contributes to the Research Methods and/or Analysis requirement for the Concurrent Certificate in Applied Social Sciences Research. As such, it relies heavily on statistics as the main tool to think about research design. Students are expected to have taken at least POLSCI 3NN3 – Statistical Analysis of Primary Data or have equivalent experience with statistics and statistical programming software. I expect us to understand the main findings of a quantitative social science study and work backwards from there to discuss the research design choices that researchers made. I also expect, but not assume, some experience with loading, cleaning, and analyzing data.
Objectives
By the end of the course students should be able to:
- Understand the components of a research design and its properties
- Read and critically evaluate research outputs about public opinion and policy
- Work fluently with statistical programming software and learn new techniques on their own
- Design, evaluate, and implement quantitative studies using the workflow proposed in this course
Materials
Reading
The main textbook we will follow is:
- Blair, Graeme, Alexander Coppock, and Macartan Humphreys. 2022. Research Design in the Social Sciences: Declaration, Diagnosis, Redesign. Princeton University Press
You can read the digital copy of the book for free by using the link above or purchase the physical version if you choose. Purchasing the book is not necessary to succeed in this course. The rest of the syllabus refers to this book with the initials RD.
The companion textbook is:
- Grolemund, Garret, Mine Çetinkaya-Rundel, and Hadley Wickham. 2023. R for Data Science. O’Reilly Media Inc
Once again, you can read the digital copy of this book for free with the link above. You are not required to read this book for this course but consulting it on occasion may help you overcome hurdles while working with statistical software. Since the digital version is constantly updated, I do not recommend buying a physical copy.
We will also read academic papers that discuss or apply the research designs we will cover. Most of these are available through the library’s subscription. See this link for instructions to access library resources while off campus. If not available through the library or elsewhere online, I will upload them to the course website.
Software
We will use R and RStudio to program research designs and evaluate their properties. The advantage of R is that it is free and open source, meaning that you will be able to apply everything you learn in this course anywhere else. The disadvantage is that it has a somewhat steep learning curve. I believe the investment is worthwhile for anyone working with data or in data-adjacent careers.
The computers in our classroom should have a recent installation of both programs. While I expect us to spend some class meeting time working with R, you will most likely need access to the software outside of the classroom. You are welcome to bring a laptop to class.
You can install R and RStudio in your personal computer. You can use this link for installation instructions on Windows and MacOS (ignore the parts about package building). See this link for installation instructions on Chromebooks, which is a bit more involved.
You can also use Posit Cloud to access RStudio from any web browser. A free account should be sufficient for the purposes of this course and has the advantage of letting you access your work across devices.
You should reach out to the instructor if you foresee any challenges with accessing computing resources outside of the classroom.
Evaluation
This course uses a labor-based grading agreement, commonly known as contract grading. See the Evaluation page for more details. Your final grade will depend on the following components.
- Attendance and participation
- Weekly lab assignments, due on Mondays at 11:59 PM
- Response papers, due on Wednesdays at 8:30 PM
- Optional final project: Pre-analysis plan, due April 25 at 11:59 PM
Grades will be based on the McMaster University scale:
| Mark | Grade | Mark | Grade |
|---|---|---|---|
| 90-100% | A+ | 63-66% | C |
| 85-90% | A | 60-62% | C- |
| 80-84% | A- | 57-59% | D+ |
| 77-79% | B+ | 53-56% | D |
| 73-76% | B | 50-52% | D- |
| 70-72% | B- | 0-49% | F |
| 67-69% | C+ |
Policies
Submitting assignments
Prompts for assignments will be available in the course website. You should upload assignments via Avenue.
Assignments should use the author-date citation style of the Chicago Manual of Style. You do not need to include citations for the weekly lab assignments, but you can do so if you wish.
You can use the templates available in the Resources tab to format your assignments, slight modifications within RStudio are acceptable. If you write assignments outside of RStudio, they should be double-spaced, 12 pt font, with 1-inch margins. Assignments do not require a cover sheet. Figures, tables, and bibliography are not part of word counts. You can use this tool to count words in PDF documents.
Late assignments
In this course, assignments are designed to be cumulative; each assignment builds on the last. For this reason, it is important to not fall behind and to complete assignments on time. Assignments are considered delayed if they are submitted after one hour but within 24 hours of the due date. Assignments will be considered late if they are submitted after 24 hours but before 7 days of the due date. Assignments not submitted within 7 days of the due date will be considered as not delivered.
Use of the MSAF form will automatically move the due date 72 hours, with no other possibility of extension or late submission without additional confirmation of the circumstances by the Faculty advising office. If you use the MSAF form for an assignment, you do not need to email me. Just turn in the assignment within 72 hours via Avenue or to the Political Science office (KTH 527). There is a drop box for after hours.
Other policies
See the PDF version of the syllabus for McMaster University policies that may apply to this course.