Syllabus
Current as of 2025-09-25
Lecture: MW 10:15am-11:45am (PCPSE 203)
Dr. Marc Trussler
Fox-Fels Hall 32 (3814 Walnut Street)
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Office Hours:
- Monday 2-4pm (in person).
- Thursday 1-4pm (virtual, create appointment here).
Course Description
Surveys are fundamental in organizing democratic societies. While much can be learned from administrative data, in order to understand the attitudes and behaviors of a population we must turn to survey research. While much of society’s image of surveys is wrapped up in pre-election polls (which is something we will cover in depth!) surveys are used to estimate the unemployment rate, assess public opinion about current events, to understand the demographics of the country, to generate television ratings, and much more. Virtually every industry uses surveys in some way to understand the world.
This class will teach you how to design, conduct, analyze, and report on surveys. Students will learn how to be critical consumers of information derived from surveys. To gain practical experience, students in the course will collaborate together to run and report on an original survey as the final project of this class.
The course will begin with an overview of the history of surveys in society, as well as the basic principles of survey design. We will then discuss the basics of questionnaire design, as well as the fundamentals of probability and survey sampling needed to correctly interpret survey information. We will cover the (increasingly) necessary step of survey weighting, and cover areas where that weighting can fail to correct for important biases. Alongside the November election we will cover political surveys in depth and take a deep dive into current survey results using our new knowledge. In the second half of the semester we will cover a number of special topics like social desirability bias, survey experiments, panel surveys, and the use of “post-stratification” to use surveys to estimate opinion in subsets of the population.
The course will be an equal mix of theoretical and technical. There is a good amount to learn here about how people think and how we can use the imperfect tool of a survey question to access those thoughts. We will also work through the statistical techniques needed to estimate the uncertainty in a poll, particularly under different sampling techniques and when the “ideal” sampling assumptions break down. Further, we will work through the nitty-gritty technical process of receiving, cleaning, weighting, and presenting survey data.
While we will focus on these theoretical and technical details, the end goal is to be able to generate information from surveys for a non-technical audience.
Prerequisites
PSCI 1800 or equivalent approved by the instructor. You should have experience performing the following tasks in the R programming language: loading data, running loops, sub-setting data, generating new variables, merging datasets, and calculating descriptive statistics. We will spend a week brushing up on some helpful skills for this class, in particular the use of loops to estimate probability and the use of the tidyverse package to manage a dataset.
Expectations and policies
Course Slack Channel
We will use Slack to communicate with the class. You will receive an invitation to join the our channel shortly after the start of class. One of the better things to come about through the pandemic is the use of Slack for classroom communications. It is a really good tool to allow us to send quick and informal messages to individual students or groups (or for you to message us). Similarly, it allows you to collaborate with other students in the class, and is a great place to get simple questions answered.
Because we will be making announcements via Slack, it is extremely important you get this set up.
Format/Attendance
There are two in-person lectures each week. The lectures will not be recorded, though this textbook contains my notes, and any accompanying R code will be provided. There is no need to inform me if you are going to miss a class.
While it is your decision whether or not to attend class, you are responsible for the knowing the things that I say in class. This includes any announcements or clarifications I make on assignments or tests.
Far more than other courses that I teach which you may have taken, this is a discussion based class. As is covered below, you will be graded on your participation in class discussions (and therefore, implicitly, on your attendance). That being said, a perfect score on participation does not require a perfect attendance record.
Academic integrity
We expect all students to abide by the rules of the University and to follow the Code of Academic Integrity.1
Details on what constitutes an academic integrity violation will be covered on an assignment-by-assignment basis.
Re-grading of assignments
All student work will be assessed using fair criteria that are uniform across the class. If, however, you are unsatisfied with the grade you received on a particular assignment (beyond simple clerical errors), you can request a re-grade using the following protocol. First, you may not send any grade complaints or requests for re-grades until at least 24 hours after the graded assignment was returned to you. After that, you must document your specific grievances in writing by submitting a PDF or Word Document to the teaching staff. In this document you should explain exactly which parts of the assignment you believe were mis-graded, and provide documentation for why your answers were correct.We will then re-score the entire assignment (including portions for which you did not have grievances), and the new score will be the one you receive on the assignment (even if it is lower than your original score).
Assessment and grading
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Weekly Practice Questions (0%)
There will be a few weekly practice questions each week that will serve as preparation for the midterm and final exam. These will not be submitted or graded, though I encourage you to come talk to me during office hours to get feedback on your answers. It is your responsibility to use these questions (and my feedback) to “know what you don’t know”.
Participation (15%)
In class participation will be scored on a class-by-class basis. The I will take contemporaneous notes following lectures on who actively participated. The quantity, and in particular the willingness to take a swing at trying to ask and answer questions, will matter more here then the quality or “rightness” of your participation.
At mid-term I will let you know how you are doing on participation, and you will have the opportunity to improve your score over the back-half of the course, if necessary.
If you are not fully comfortable with in class participation, I will take into account your attendance at office hours and participation in the course slack as well.
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Midterm (20%)
An in-class exam that will take place during our usual class period on October 15th.
The test is (partially) closed book, and will be hand-written using a blue book.
You will be allowed a one sheet of paper “cheat-sheet”. That is, you can bring in whatever notes you would like as long as it fits on a single sheet of 8x11 paper.
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Final Exam (30%)
A 2-hour in-class exam that will take place during the final exam period on TBD.
The test is (partially) closed book, and will be hand-written using a blue book.
You will be allowed a one sheet of paper “cheat-sheet”. That is, you can bring in whatever notes you would like as long as it fits on a single sheet of 8x11 paper.
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Final Project (35%)
- Due: December 5. The final project of this course will be the culmination of a semester long project to collect and report on survey data. Please see the rubric on canvas for specific details on how this project will be scored. In short, we will field our own survey at the beginning of November. I will assign you to teams to write a battery of survey questions with a specific theme. These survey batteries will be fielded and we will collect the results. Individually, you will take this data and clean, weight, analyse, and present the information you find interesting. The final project will consist of your cleaning script, a cross-tab and methodology document, and an article (similar to one that would be written for NBC) discussing the findings.
Grade scale
Letter grades at the conclusion of the class will be assigned using the following scale. I do not round grades. If your grade is in one of the bands below you will receive that grade.
\[\begin{aligned} 97 \leq Grade: &A+\\ 93 \leq Grade <97: &A\\ 90 \leq Grade <93: &A-\\ 87 \leq Grade <90: &B+\\ 83 \leq Grade <87: &B\\ 80 \leq Grade <83: &B-\\ 77 \leq Grade <80: &C+\\ 73 \leq Grade <77: &C\\ 70 \leq Grade <73: &C-\\ 67 \leq Grade <70: &D+\\ 63 \leq Grade <67: &D\\ 60 \leq Grade <63: &D-\\ Grade <60: &F \end{aligned}\]Computing
The course will require students to have access to a personal computer in order to run the statistics software. If this is not possible, please consult with one of the instructors as soon as possible. Support to cover course costs is available through Student Financial Services.
We will use R in this class, which you can download for free at https://www.r-project.org/. R is completely open source and has an almost endless set of resources online. Virtually any data science job you could apply nowadays to will require some background in R programming.
While R is the language we will use, RStudio is a free program that makes it considerably easier to work with R. After installing R, you will install RStudio https://www.rstudio.com. Please have both R and RStudio installed by the end of the first week of classes.
If you’re having trouble installing either program, there are more detailed installation instructions on the course Canvas page.
Textbooks
The reading load for this course will consist of chapters from a textbook and supplementary readings (usually journal articles) that will be posted on canvas.
Unlike other classes you may have taken with me: you are responsible for knowing the content of these readings. That is, there may be things in these readings that I do not cover in class that I nevertheless ask about on the exams.
The textbook for the class is widely available to buy, but is also available for free on the library website.
Groves et al. Survey Methodology, 2nd edition. 2009.
Course Schedule
September 15
Questionnaire Design I
Groves Chapter 7
Zaller and Feldman. 1992. A simple theory of the survey response: Answering questions versus revealing preferences. American Journal of Political Science.
September 22
Questionnaire Design II
Groves Chapter 8
Perez. 2016. Rolling off the Tongue into the Top-of-the-Head: Explaining language effects on public opinion. Political Behavior.
September 29
Sampling and Probability I
Groves Chapter 3
Squire, Peverill. 1988. “Why the 1936 Literary Digest Poll Failed.” Public Opinion Quarterly 52(1): 125–33.
October 20
Survey Non-Response and Weighting
Groves Chapter 6 and 10.5
Bailey. 2024. Polling at a Crossroads: Rethinking modern survey research. Chapter 3.
October 24: Grade Mode Change Deadline
October 27
Political Polling
- AAPOR Task Force on 2020 Pre-Election Polling. “An Evaluation of 2020 General Election Polls”
Questionnaires for Final Projects Due October 27 (Might change as I work with survey firm.)
November 10
Advanced topics in weighting
Bailey. 2024. Polling at a Crossroads: Rethinking modern survey research. Chapter 6.
Clinton, Lapinski, and Trussler. 2022. Reluctant Republicans, Eager Democrats? Partisan nonresponse and the accuracy of 2020 presidential pre-election telephone polls. Public Opinion Quarterly.
November 17
Survey Experiments
- Anoll, Engelhardt, and Israel-Trummel. 2025. From Protest to Child Rearing: How movement politics shape socialization priorities. American Political Science Review.
November 24 (Monday only)
Social Desirability Strategies
- Jackman, Simon, and Bradley Spahn. 2018. “Why Does the American National Election Study Overestimate Voter Turnout?” Political Analysis.