Teaching Portfolio

Policy | Resilience | Sustainability | Data Science | Mixed Methods

Timothy Fraser

Dept. of Political Science
Northeastern University

Table of Contents

1 Teaching Statement

As an educator, I rely on active learning methods to help students learn political science, data science, and methods. The ‘active learning’ I employ in my classroom refers to intense focus on student-led learning, as opposed to a lecture format. Below, I outline my teaching practices, built through teaching experiences and student research mentorship.

Teaching Experience: I have taught 3 courses at Northeastern University: Quantitative Techniques for Political Science (Spring 2020, Fall 2021), and Research Methods (Summer 2021). I have taught as a teaching assistant for 8 courses, including Comparative Politics, Quantitative Techniques, American Politics, and a study-abroad program in Japan.

In course evaluations, my students rate my teaching highly (an average of 4.65 / 5, with a standard deviation of 0.16). Students rated my work above the department and university averages in several dimensions, including:

  1. teaching effectiveness

  2. effective use of class time

  3. feedback quality

  4. recommending me to other students

  5. respectful & inclusive learning environment

  6. effective action when students do not understand material

  7. availability outside of class

  8. my enthusiasm for the course.

Active Learning in the Classroom: In my courses, I dedicate two-thirds of class time to active learning exercises, using an adapted reverse-classroom form. (I reserve the remaining third for lessons, class discussion, and weekly visits from guest scholars.) I use three forms of active learning: workshops, labs, and projects, discussed below.

Active Learning through Workshops: Once a week, students conduct a workshop. Together in small groups, they learn and apply new techniques based on the lesson content. For example, in Research Methods, students map local businesses. In Quantitative Techniques, students learn new visualizations in R. In comparative politics, students might role-play a social network to learn how social capital affects voting.

Evaluations show students value my reverse-classroom style, saying:

“[he] was really patient with us as we all learned how to code… I appreciate how much he cared about our learning and efforts he made to make sure we understood and learned to apply our material.”

Discovery-based Learning through Labs: Later in the week, students apply their learning in labs to a contemporary social science question. This second layer of active learning solidifies workshop knowledge and asks them to discover how to use prior tools to solve new problems. My methods students deployed survey experiments to test marketability of a new Dunkin’ Donut flavor. Other labs focus on policy, like using the difference of means to test effects of the Fukushima disaster on Japanese cities’ renewable energy adoption. A few times, I ask students to summarize results into short 2 lab reports, emulating the methods, results, and discussion format of research papers. This helps them practice articulating their research’s value-added, limitations, and future directions.

In evaluations, students highlight my active course design and class structure:

“Professor Fraser does a great job incorporating different teaching styles and activities to keep the class fresh,” citing “labs, group-work, discussion, [and] lecture.”

Others reported that ’[he] was always enthusiastic and prepared… [and] structured class time in a way that was really engaging. I really feel like I learned a lot from him.”

They also indicate that I am flexible and responsive: “He listens to student responses about what he can do better and communicates well with students.”

Others said I taught engaging virtual classes, providing “a really good variety of activities, including breakout rooms, lectures, discussions, guest lecturers, readings, videos, and in-class labs that were helpful in making the class interesting/informative… it was actually really engaging.”

Building Inclusivity and Social Capital through Projects: For term projects, I commission these groups to answer a real policy question with empirical data (past groups examined crime rates, terrorism, emissions, and partisanship, for example). These projects are valuable beyond content alone: my projects, workshops, and labs build social capital and trust among team members. I use frequent peer reviews, short group presentations, and honor codes to circulate feedback and norms, raising the quality of everyone’s work. These groups, whose members stay the same all term long, buoy students as they celebrate together the act of completing their first line of code, visual, experiment, or case study.

Using these groups, I build an inclusive atmosphere that helps my students feel valued and respected, especially students with accommodations or from under-represented backgrounds. I incorporate discussions themes like racial discrimination, sexism, mental health, and different ways of learning to normalize and affirm respect for the diversity of our classroom and society. And, I use group discussions to help students practice using new language and build these norms together. Further, I have invited 21 weekly guest speakers to my courses; these talks also normalize the expertise of women (75% of speakers) and people of color (33%) in the social sciences, and help students envision themselves in the field.

My evaluations show that students value this atmosphere. One student wrote that I “did a wonderful job of creating a welcoming and inclusive atmosphere.”

Others valued my attention to accessibility, saying, “[he] is really mindful of all his students’ different situations and that really reflects in his teaching,” and “he was always available to help us better understand the course.”

Research with Students: In addition to teaching, I deeply enjoy research with students. At Northeastern, I ran 3 capstones for 10 public policy masters students. Meeting weekly, I trained student teams to record data and analyze results. Together, we produced publishable papers from each capstone. My capstones covered (1) Louisiana recovery policies after Hurricane Katrina, (2) membership on disaster recovery committees in New York City after Hurricane Sandy, and (3) mapping Boston community spaces.

Also, I have conducted my own research with 8 talented undergraduates, producing 8 peer-reviewed studies. In May 2020, I started an ‘Environmental Politics Working Group’ with students, taught them statistics,predicting solar adoption, greenhouse gas emissions, and disaster vulnerability in Japanese cities. I also advised a senior capstone, helping a new interviewer design her questions for fieldwork in Japan and write 2 publishable studies. I led student engagement on two multigenerational research teams, coauthoring with undergraduates to publish 3 studies on social networks and disasters.

What I bring: At my next post, I can bring ready-to-go courses on (1) Research Methods and (2) Quantitative Techniques in R.

In Research Methods, my students apply 10 major methods, including interviews, surveys, social networks, and GIS, in labs about disasters, renewable, and pandemic recovery. In Quantitative Techniques, my students learn to code statistics and visualizations in R using labs on the Fukushima crisis, emissions, crime, and health, culminating in conference posters. (I can also teach any courses related to Public Policy and Comparative Politics, which were my PhD subfields.)

I would be especially excited to develop a course on Climate Change and Urban Resilience, Energy Policy, Social Capital and Pandemic Resilience, GIS, Social Network Analysis, or a capstone course, where I could lead several teams in learning a new method and then providing real empirical research for local community partners and producing publishable research. Through my teaching, I aim to help students build data-driven toolkits for solving key environmental policy issues in our communities today.

2 Diversity Statement

As an educator on environmental politics and data science, I promote diversity in the classroom with 3 main strategies. My teaching practices focus on 1) multimodal teaching to support a neurodiverse student body, 2) building a village-mindset of learning, and 3) boosting representation from women & under-represented minorities in the classroom.

Embracing Neurodiversity

My teaching approach centers on making my classes welcoming and accessible to a neurodiverse student body. “Neurodiversity” refers to the idea that brain differences among people are normal, not deficits; given that nearly 20% of the population is neurodivergent (having ADHD, autism, dyslexia, learning disabilities, or other neurological differences) and 25% have a diagnosed mental health condition (eg. anxiety or depression), this indicates that neurodivergent students are much, much more common in the classroom than traditionally perceived. To respond, I design my classes with broad, sweeping improvements to be more accessible to students who think and learn differently.

Write it Down

First, I present all teaching content written down, sharing key terms and reading questions using slides with explicitly-written definitions, while students follow along from their computers. First, for students with ADHD, anxiety, depression, and other conditions that impact attention, conveying key information verbally can be quite challenging, because it asks students to never miss what is said verbally and implicitly punishes them when they lose focus because of their differences. Providing written content gives neurodiverse students permission to ‘hyperfocus’ on one specific part or zone out on another, without costing them the entire lecture, because they can see written down what happened when attention wandered and immediately catch up. For students with dyslexia, dysgraphia, dyscalculia, or non-verbal learning disorder, it gives them more time to read and process words, numbers, or graphs, and the chance to revisit past slides immediately. For my other “neurotypical” students, it is equally helpful, reducing stress to catch every detail and instead allowing them to take meaningful notes and reflections.

Beyond Visual Learners

Second, I have adapted my courses to better teach visually-impaired students (students with limited vision). In typical classes, visually-impaired students just receive a note-taker and professors continue teaching as usual on boards too far away to see. Instead, I make all material available in real time through a web browser, custom coding my Workshop’s HTML code to allow students to zoom, adjust text sizing, and actively participate. This has the benefit of making my class extremely accessible to students in quarantine during COVID; since they know that their learning will not be impacted, this is an incentive for them to quarantine when necessary to protect their community.

Multi-modal teaching

To further address learning disabilities in the classroom, I use a range of teaching modalities every day. Each week, I spend just a third of time on lessons (verbal instructions with slides), and focus instead on small group presentations. These ask students to take ownership of material in small groups, respond to reading questions, briefly present to classmates, and discuss. I also use tactile learning exercises; one favorite is to teach students about sampling distributions, taking random samples of Skittles and calculating the share that are red.

The majority of class time is spent on hands-on active-learning workshops and labs. There, I ask students to practice interviewing each other, or asking quantitative techniques students to work together to visualize and analyze a new political dataset using new coding techniques in R. This is because students, and especially neurodiverse students, tend to learn better by doing a task multiple times, not hearing or reading about it multiple times. My students have responded enthusiastically to this skill-based teaching style.


Breaking up the pace

Further, I break up the pace of class using frequent breaks, activities sectioned into 20 minutes, and short guest talks, to help students set-switch between tasks, avoid overwhelm, and allow hyperactive students to move about. Such measures were transformative when I taught a class during the pandemic with several neurodivergent students in 3-hour long class sessions over Zoom. Three hours is rough for most students, especially those with hyperactivity or learning disabilities. My measures in the classroom help my neurodiverse students and students with disabilities grow their skills and demonstrate their creativity, rather than punishing them with a stringent, unyielding classroom framework.

It takes a village

Because I study the value of social capital and social networks, I promote diversity in the classroom by building strong relationships among students, because ‘it takes a village’ to make good social science and to learn to code. Women and students from under-represented minorities have often been underserved or excluded in past learning settings; to address this, my students work in small, gender-and-racially-diverse groups every day, building close peer-groups to trust, support each other, and normalize common struggles. I emphasize stories of ordinary students, not ‘whiz kids,’ arguing that people learn to code together, relying on encouragement from mentors, peers, and family. Together, students learn professional skills in methods and data science they never realized they could do alone.

Representation

Finally, I make several efforts to increase representation of under-represented minorities in the classroom. Between my methods and quantitative techniques classes, I have invited dozens of weekly guest speakers, especially including women (75%) and people of color (33%) to share their experiences using research methods, quantitative techniques, and coding. These speakers, hailing from Northeastern, Johns Hopkins, Dartmouth, Arizona State University, and Google, provide role models, give students the chance to envision themselves in these fields, break down gender and racial hierarchies in data science, and implicitly tell my students that diversity is the norm, and that they are valued in this field. Similarly, I assign readings from a diverse array of scholars. In class, I require students specifically to use scholars’ full names to humanize these scholars and discuss their experiences and contributions to the field. This helps emphasize the presence of women and people of color in the field.

Student Research

Finally, and most importantly, through my classes, I have invited and worked with 19 undergraduate and masters students on publishable research papers. I specifically aim to support the talent of underrepresented students. 60% of my student coauthors are women and 66% of from under-represented minorities. Our work together has produced 8 peer-reviewed studies published in top journals of environmental politics, and helped my students build hireable quantitative skill sets.

Using 1) multimodal, inclusive teaching strategies, 2) building social ties in the classroom, and 3) increasing representation from women and under-represented minorities in the classroom and the curriculum, my teaching methods help students of different backgrounds build skills in data science, methods, and policy analysis, growing their confidence and increasing their long-term professional opportunities.

3 Course Descriptions for 3 New Courses

The following proposed courses are all directly based on my past research and teaching experiences, and I am eager to teach these given the opportunity!

Climate Change and Urban Resilience

[Public Policy / Comparative Environmental Studies Course]

Why do some cities respond to, adapt to, and recover from the impacts of climate change better than others? This class introduces students to theories of community resilience, especially the role of social capital and social vulnerability in climate crises.

Mapping Green Communities with GIS

[Good crossover course for Urban Policy / GIS / Environmental Studies]

Mapping is a key asset in solving environmental challenges. This class will introduce students to basic mapping and geographic information systems techniques, taught in the R coding language (or in ArcGIS, if preferred). (No prior coding experience necessary). Students will learn to work with point, polygon, and raster data, basic visualization, spatial joins, hotspot analysis, and spatial interpolation.

Social Network Analysis for Environmental Policy

[Good crossover course for Data Analytics / Environmental Studies]

Networks are all around us! This course introduces students to basic social network analysis techniques in the R coding language (no prior coding experience necessary), through examples from environmental studies.

4 Courses Taught

Undergraduate Courses

  • Hands-on learning with weekly labs, workshops, discussions, and guest lectures. Students collaborate in groups to analyze topical policy issues.

  • Frequently integrates my research on cities, environmental, and data science.

Quantitative Techniques (Spring 2020, Fall 2021)

Research Methods (Summer 2021)


Masters Capstones

  • I have led 3 capstones for the School of Public Policy and Urban Affairs at Northeastern University, acting as the ‘client’ coordinating groups of masters students as they develop a plan to analyze a requested social/policy issue.


Mapping Social Infrastructure in Boston Neighborhoods (Fall 2021)

  • Working with Tim, students developed an online map of community spaces, places of worship, social businesses, and parks in Boston.

Disaster Recovery Committees after Hurricane Sandy

  • Together with Tim, students collected data on 600 members of local disaster recovery policy committees in NYC neighborhoods, and measured policy differences with text analysis.

Uneven Paths: Recovery in Louisiana Parishes after Hurricane Katrina

  • Together with Tim, students coded Louisiana Parishes’ recovery strategies after Hurricane Katrina, and tested effects on recovery with Synthetic Control Experiment. (Won departmental award!)

5 Teaching in the Field

Study Abroad Program Assistant


Course: Disasters and Recovery in Japan (Summer 2018)

  • Destinations: 4 cities in 4 weeks! Tokyo, Sendai, Kobe, and Hiroshima, with day trips to nearby communities.

  • Responsibilities: Led student research advising, translation, and day-trips to field sites to meet with residents and local business representatives (eg. in Ishinomaki, Japan, pictured below).



4 Cities, 4 Weeks!


Learning about Recovery from Local NGO, Ishinomaki, Japan


Learning on the Bullet Train!


Teaching Team

6 Research with Undergraduates

Projects

  • Japanese Greenhouse Gas Emissions (2020)

  • Disasters & Renewable Energy (2020)

  • Mayors & Renewable Energy (2020)

  • Hokkaido Earthquake Evacuation (2020-2021)

  • Tohoku Recovery Committees (Fall 2019, Spring 2020)

  • Mapping Social Infrastructure (Fall 2021)

  • Tokyo Environmental Committee Networks (Summer 2021)

  • Effects of Social Capital on Vulnerability in Japanese Cities (Summer 2021)

  • Gender Equity in the ADVANCE Network (Fall 2020)


Frequent Student Coauthors


Team Publishing

Together, these students and I applied data science techniques and published 8 peer-reviewed studies in top journals in environmental policy, including:

  • Global Environmental Change
  • Environmental Innovations & Societal Transitions
  • Climate Risk Management
  • Journal of Environmental Management
  • Global Environmental Politics
  • and more!

Spotlight: Student coauthor Larissa Morikawa’s work on Evacuation




Topic: Evacuation after 2018 Earthquake in Hokkaido, Japan

Research: Advised Larissa on her fieldwork in Hokkaido, as she learned interviewing, content analysis, and case studies for our mixed methods paper together.

  • Timothy Fraser, Larissa Morikawa, and Daniel P. Aldrich (2021). Rumor has it: The role of social ties and misinformation in evacuation to nearby shelters after disaster. Climate Risk Management 33, 100320. [Open Access]

  • Timothy Fraser, Daniel P. Aldrich, and Larissa Morikawa. (2021). Do All Roads Lead to Sapporo? The Role of Linking and Bridging Ties in Evacuation Decisions. Ecology & Society. Accepted December 7, 2021.


Spotlight: Student coauthor Andrew Small’s work on Disaster Recovery









Topics: Disaster Recovery after Japan’s 2011 Triple Disaster

Research: Advised Andrew in online data collection for large-N network analysis project and in developing case studies about disaster reconstruction committees. Together, we published 3 mixed methods studies.

  • Timothy Fraser, Daniel P. Aldrich, Andrew Small, and Andrew Littlejohn. (2021). In the Hands of a Few: Disaster Recovery Committee Networks. Journal of Environmental Management 280, 111643.

  • Timothy Fraser, Daniel P. Aldrich, and Andrew Small. (2021). Connecting Social Capital and Vulnerability: A Citation Network Analysis of Disaster Studies. Natural Hazards Review 22(3).

  • Timothy Fraser, Daniel P. Aldrich, & Andrew Small. (2021). Seawalls or social recovery? The role of policy networks and design in disaster recovery. Global Environmental Change 70, 102342.

7 Hands-On Learning with Dashboards

Using Statistical Significance to Study Environmental Racism in US Counties

  • Target Students: Undergrad Quantitative Techniques students, learning statistical significance for the first time.

  • Exercise: Students navigate my online dashboard, testing whether communities of color experience worse rates of air pollution. Students toggle dashboard options to test happens when sample size, number of samples, etc. changes. Then, students apply their knowledge to learning check questions. (Length: 45 minutes)


Using Inferential Stats to Study How Fukushima Shaped Elections

  • Target Students: Undergrad Quantitative Techniques students, with no prior knowledge of t-tests, correlation, or chi-squared tests, or p-values.

  • Exercise: Students navigate my online dashboard in groups, toggling the year and sample size to see how the difference in mean voter turnout changed before vs. after Fukushima. Students learn intuitively the meaning of p-values by visually comparing how extreme their test statistic is. Repeats for chi-squared and correlation. (Length: 45 minutes)

8 Sample Lessons


Lesson: Social Network Analysis

  • Course: Research Methods (Summer 2021, Virtual-Synchronous)
  • Topics Covered: Social networks, matrices, applications of networks to disaster social science.
  • Watch here on Youtube!)


Lesson: Writing about Statistics

  • Course: Quantitative Techniques (Fall 2020)
  • Topics Covered: Reporting statistical model results, heteroskedasticity, and summarizing results for a general audience.
  • Watch here on Youtube!)


Lesson: Uses of Mapping

  • Course: Research Methods (Summer 2021)
  • Topics Covered: Learn about concepts of spatial data, mapping, fishnet grids, spatial smoothing, and hotspot analysis.
  • Watch here on Youtube!)


Lesson: Big Data

  • Course: Research Methods (Summer 2021)

  • Topics Covered: Case study on promises and perils of using big data to analyze social trends. With examples from measuring evacuation, criminal justice, and more.

  • Watch here on Youtube!)

9 Sample Workshops

27 Ready-Made Coding Workshops for Undergratuates

I have created 27 publicly available online workshop tutorials on coding, visualization, modeling, and GIS in R through my RPubs website.


What’s in a Workshop?

  • Each workshop is designed to take about 1 hour to complete, and can be assigned as an in-class, hands-on group activity or homework.

  • Workshops are interactive, filled with learning checks and coding challenges, and provide answers for students to check their work against.

  • Workshops can be used sequentially in a class, or on a case-by-case basis to support students doing a final project or capstone.

  • Workshops require students to directly apply their knowledge, and help them build a portfolio of data visualizations they have produced.

  • Each workshop comes with a ready-made RStudio.Cloud project and data they can use to start coding immediately from any browser window.



Workshop: Testing Effects of Disaster on Social Capital with Multiple Regression

Click here to View Workshop Online!


  • Target Students: undergraduate social science students who just learned bivariate regression, and now must learn multiple regression.

  • Case Study:: Change in Social Capital over time in coastal Japanese cities after 2011 disaster

  • Learning Goals:: Students will learn usage and importance of control variables in multiple regression, when and when not to compare effect sizes, and how to compare models in a statistical table. Students will learn to code all these tasks.



Workshop: Using Statistical Simulation to Predict Food Deserts

Click here to View Workshop Online!


  • Target Students: undergraduate social science students who have recently learned to code multiple regression.

  • Case Study:: Simulate Effect of Racial Inequity in Food Insecurity and Food Access, using US County Health Measures

  • Learning Goals:: Students will learn to make statistical simulations in Zelig package in R. Students will learn difference between predicted and expected values, and learn how it works by making their own simulation from scratch, without Zelig.

Workshop: Mapping Boston Social Infrastructure

Click here to View Workshop Online!


  • Target Students: undergraduate social science students who have learned basic modeling and visualization in ggplot2.

  • Case Study:: Map spatial variation in access to community spaces, places of worship, parks, and social businesses in Boston

  • Learning Goals:: Students will learn to wrangle spatial data with the sf package & map using ggplot2. Students will also learn basic spatial joining and use it to make their own models.


Lab: Political Polarization in US Counties

Click here to View Lab Online!


  • Target Students: undergraduate social science students who have learned basic visualization and data wrangling in ggplot2 and tidyverse packages in R.

  • Case Study:: Visualize increasing partisan polarization in the US using county elections data from 2000 to 2020.

  • Learning Goals:: Students will wrangle, visualize, and interpret data to show which regions see steepest increases in polarization. Students will then write up their findings in a 2 page report.

10 Sample Syllabi

Syllabus: Quantitative Techniques


POLS 2400: Quantitative Techniques, Northeastern University

  • 9:50 - 11:30 AM, Tuesdays & Fridays, Hayden Hall 425 (in-person)
  • Professor: Tim Fraser, Instructor & PhD Candidate in Political Science
  • Email:
  • Office Hours: Sign up on at timothyfraser.youcanbook.me
  • Class Zoom Link (when needed): https://northeastern.zoom.us/j/93273600764
  • [See Course Site on Canvas for all course readings, assignments, and most up-to-date details]

Course Description

Studies methods of quantitative analysis including descriptive statistics, hypothesis testing, cross-tabulation, analysis of variance, bivariate regression and correlation, and multiple regression. Examines how to generate and interpret statistical findings through use of Excel, SPSS, and/or other software programs. Uses examples from political behavior, public policy analysis, public opinion, comparative and international politics, and other areas of political and social-science inquiry to emphasize practical applications

Course Rationale

Quantitative analytic techniques are increasingly central to work in politics, business, and nonprofits. This course introduces the most common quantitative analytic & statistical techniques use today. Starting with zero prior knowledge, students will learn to interpret social science research and conduct their own studies!

Course Aims and Outcomes

  • Develop and apply statistical research skills related to the study of political science
  • Interpret the use, misuse, and spin of numbers in journalism, policy reports, and arguments.
  • Make descriptive statistics, hypothesis tests, and regression
  • Read and interpret results of these analyses
  • Learn basic programming for visualizations and statistics in R, a popular coding language!

Course Structure:

  • Classes: In-person, 9:50 - 11:30 AM @Hayden Hall 425 (9/08 - 12/08)
  • Course Website: Canvas - bookmark this website, and plan on using it everyday.
  • Readings: Free, on Canvas!
  • Office Hours: Sign up at timothyfraser.youcanbook.me
  • RStudioCloud: Our online program for stats, available here.

Course Pattern:

  • Lessons: Tuesdays (first half), we learn new techniques through lecture and group discussion. Readings are always due for the first class of each week.
  • Workshops: Tuesdays (second half), we workshop new techniques together in small groups.
  • Discussions: Fridays (first half), we discuss ways statistics have been applied, and meet with guest researchers who use statistics in their work.
  • Labs: Fridays (second half), we apply new techniques in “coding labs” as teams or individuals.

Grades and Assignments (See Assignments page on Canvas for full details):

  • Homework (10%)
  • Quizzes & Exams (30%)
  • Lab Reports (30%)
  • Research Project (Proposal, Paper, Poster, Video) (20%)
  • Participation & Presentations (10%)
  • Bonus (max +4%)

How should I sign up for Office Hours?

  • Learning new skills requires some self-teaching on your part, and that can be challenging. To help with this, I will be holding virtual office hours during the times listed on my calendar here.
  • These office hours will be one-on-one, and you can book them in 20 minute intervals. You may book multiple time slots, but please be considerate of your classmates who may also wish to speak with me. When using virtual office hours, please follow some basic decorum. - For instance, please be fully clothed and please do not call from in bed.
  • You may book office hours with me at the following url: https://timothyfraser.youcanbook.me
  • All virtual office hours will be conducted on Zoom at the following link: https://northeastern.zoom.us/j/93273600764

Course Philosophy

Your success in this class is important to me! We all learn differently. If there are aspects of this course that prevent you from learning or exclude you, please let me know ASAP. Together we’ll develop strategies to meet both your needs and the requirements of the course!


You will learn some basic quantitative techniques skills, including coding, visualization, and statistics, etc. (exciting!) This course is designed to support you in learning these new skills via office hours, small groups, our textbook online, and workshops and labs.


Students should regularly come to office hours. If you have questions about what we learn one day, come to office hours that week, because we will build on this knowledge in the next day.


You are welcome in this class. I expect professional courtesy and sensitivity from everyone in this class, especially over differences of race, culture, religion, politics, gender, sexuality, disability, and nationality - you name it. Sometimes key information hasn’t reach me; I will gladly honor your requests to address you by your preferred name and gender pronouns. If you learn differently, you are not alone; I am here to support you in your learning.


100 minutes is a lot of class! I promise to bring 150% enthusiasm, timely and relevant examples and readings, group workshops, frequent ‘labs’ where we can practice coding, peer review where your colleagues will review each other’s work and bolster it, breaks to help us get recharge, and exciting guest speakers to talk with about doing real social science out in the world.


In exchange, I need to ask of you: - Bring your enthusiasm, be present, & stay engaged! - Get to know your fellow students! We will work together in small groups every day. - Be kind and courteous, and support each other! - Come prepared! Do the prepwork and readings before classes, and come prepared with questions, especially for our guest speakers!

Learning with COVID

  • If you ever feel under the weather, please do not come to class. We will always make it work. Follow NU protocol, get tested, and, of course, wear a mask.
  • You can still tune into our class via our class Zoom link, and you’ll still have access to the lesson and workshop materials. Let’s be safe, and take care of one another! We’re in this together.
  • Please follow proper mask etiquette.

Trust & the Honor Code:

Communities, democracies, and especially classrooms are built on trust. I know that you and I are putting in a lot of effort to work through this pandemic, and it is draining beyond what we show. Through this class, we are going to build trust in each other - trust that I am here to support your learning, that your group-mates are there to support you, and that you are here to help your class get through this too.


One way you can do this is to always tell the truth. Each of you have pledged to do this when you joined Northeastern. Northeastern’s Honor Code reads: “On my honor, I pledge to uphold the values of honesty, integrity, and respect that are expected of me as a Northeastern student.”


This means no plagiarism - that means copying answers, finding answers online, passing off someone else’s work as your own, etc. Resources like Chegg or Bartleby Learn are strictly off-limits too.


Why no plagiarism? The short answer is it impedes your learning, and makes both our lives awful. I will give you a zero on the assignment and report it to the university, every time, as I am required to. Also, I’m really good at catching it. But the longer, better answer is that honesty and trust are vital to being a good friend, family member, coworker, and member of a community, and after everything that has happened since 2020, we just owe that honesty and trust to each other.


So how do we practice it? Every time you complete an assignment, I would like you to write at the very top: “I have neither given nor received unauthorized aid on this assignment. Signed: [Your Name Here].” (Only sign it with your name if it is true!) I will only grade your assignment if you have signed this pledge, because it means you give me your word. In exchange, I will always assume you have told the truth in this class. It’s that simple.


Weekly Schedule

Module 1: Descriptive Statistics & Distributions (9/10)

Readings

  • Coding: Chester Ismay and Albert Y. Kim. Chapter 1: Getting Started with Data in R. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press.
  • Stats: Klein, Grady, and Alan Dabney. (2013). Chapter 4: Detective Work. In The Cartoon Introduction to Statistics. New York: Hill and Wang.
  • Video: Making an RStudio Cloud Account

Module 2: Visualizations & Charts (9/14, 9/17)

Readings

  • Coding: Chester Ismay and Albert Y. Kim. Chapter 2: Data Visualization. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press. Links to an external site.
  • Viz: Isabel Meirelles. (2006). Excerpt from: Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Beverly, MA: Quarto Publishing Group.
  • Guest Talk: Kate Kryder, NU Data Vizualization Specialist (9/17)

Assignments: Homework 1 (2.5%), Visit Office Hours by Friday 9/17 (2%)

Module 3: Data Wrangling (9/21, 9/24)

Readings:

  • Coding: Chester Ismay and Albert Y. Kim. Chapter 3: Data Wrangling. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press.
  • Viz: Jonathan Schwabish. (2021). Distributions. In Better Data Visualizations : A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press.
  • Guest Panel: Mireya Dorado, Lily Cunningham, and Mary Bancroft, NU CSSH (9/24)

Assignments: Quiz 1 (5%)

Module 4: Enter the Tidyverse (9/28, 10/1)

Readings

  • Coding: Chester Ismay, and Albert Y. Kim. Chapter 4: Data Importing and Tidy Data. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press.
  • Viz: Jonathan Schwabish. (2021). Comparing Categories. In Better Data Visualizations : A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press.
  • Application: David Lazer et al. (2009). Computational Social Science. Science 323, 721-722.

Guest Lecture: Mary Versa Clemens-Sewall, Johns Hopkins Applied Physics Lab (10/1)

Assignments: Lab Report 1 (Group) (7.5%)

Module 5: Inferential Statistics (10/5, 10/8)

Readings

  • Coding: The Infer Package
  • Stats: Grady Klein and Alan Dabney. (2013). Chapter 7: The Central Limit Theorem, and Chapter 8: Probabilities. In The Cartoon Introduction to Statistics. New York: Hill and Wang.
  • Application: Robert D. Putnam. (1995). Tuning In, Tuning Out: The Strange Disappearance of Social Capital in America. PS: Political Science and Politics 28(4), 664-683.

Guest Lecture: Sameera Nayak, Bouve School of Health Sciences (10/1)

Assignments: Homework 2 (2.5%)

Module 6: Sampling and Statistical Significance (10/12, 10/15)

Readings:

  • Coding: Chester Ismay and Albert Y. Kim. Chapter 7: Sampling. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press. Links to an external site.
  • Stats: Grady Klein and Alan Dabney. (2013). Chapter 12: Hypothesis Testing, and Chapter 13: Smackdown. In The Cartoon Introduction to Statistics. New York: Hill and Wang.
  • Application: David W. Nickerson. (2008). Is Voting Contagious? Evidence from Two Field Experiments. American Political Science Review, 102(1), 49-57.

Guest Lecture: Jon Fraser, Google (10/8)

Assignments: Quiz 2 (Interview #1) (10%)

Module 7: Regression & the Line of Best Fit (10/19, 10/22)

Readings:

  • Coding: Chester Ismay and Albert Y. Kim. Chapter 5: Basic Regression. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press. Links to an external site.
  • Viz: Jonathan Schwabish. (2021). 5 Rules for Better Data Visualizations. In Better Data Visualizations : A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press.

Guest Lecture: David Clemens-Sewall, Dartmouth College (10/22)

Assignments: Lab Report 2 (Individual) (7.5%)

Module 8: Multiple Regression (10/26, 10/29)

Readings:

  • Coding: Chester Ismay and Albert Y. Kim. Chapter 6: Multiple Regression. In Modern Dive: Statistical Inference via Data Science. Boca Raton, FL: CRC Press. Links to an external site.
  • Application: Diana C. Mutz. (2016). Harry Potter and the Deathly Donald. PS: Political Science & Politics, 49(4), 722-729.
  • Viz: Jonathan P. Kastellec and Eduardo L. Leoni. (2007). Using Graphs instead of Tables in Political Science. Perspectives on Politics 5(4), 755-771.

Guest Lecture: Polina Beliakova, Tufts Fletcher School (10/29)

Assignments: Homework 3 (2.5%), Proposal (1%)

Module 9: Visualizing Results (11/2, 11/5)

Readings:

  • Intro to Zelig (Video)
  • Viz: Andy Kriebel and Eva Murray. (2018). Chapter 4: Keep it Simple. In #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time. New York: Wiley & Sons, Inc.

Assignments: Project Report (First Draft) (4%)

Module 10: Regression Assumptions (11/9, 11/12)

Readings:

  • Viz: Andy Kriebel and Eva Murray. (2018). Chapter 8: Iterate to Improve. In #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time. New York: Wiley & Sons, Inc.
  • Application: What’s Next [Podcast]. (2021). The Same Warped, Racist Logic Used by the NFL Is Ubiquitous in Medicine. Slate.

Guest Lecture: Nikki Naquin, NU CSSH (11/12)

Assignments: Quiz 3 (5%)

Module 11: Visualizing Results II (11/16, 11/19)

Readings:

  • Viz: Andy Kriebel and Eva Murray. (2018). Chapter 9: Effective Use of Color. In #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time. New York: Wiley & Sons, Inc.
  • Tom Siegfried. (2018). Fighting Crime with Statistics. Knowable Magazine.

Guest Lecture: Dr. Courtney Page-Tan, Embry-Riddle Aeronautical University (11/19)

Assignments: Lab Report 3 (Group) (7.5%)

Module 12: Statistics Applied (11/23)

Readings: - Application: Josh Williams and Tiff Fehr. (2021). Tracking Covid-19 From Hundreds of Sources, One Extracted Record at a Time. New York Times Open Team.

Assignments: Project Report (Final Draft) (10%), Poster (5%) [Presentations in Class on Tuesday 11/23]

Module 13: Transformations & Interactions (11/30, 12/3)

Readings:

  • Application: Alexandra Scacco and Shana Warren. (2018). Can Social Contact Reduce Prejudice and Discrimination? Evidence from a Field Experiment in Nigeria. American Political Science Review 112(3), 654-677.
  • Application: Valen Johnson (2019). Is it the end of ‘statistical significance’? The battle to make science more uncertain. The Conversation.

Assignments: Homework 4 (2.5%), Lab Report 4 (Individual) (7.5%)

Module 14: Building Your Portfolio

Assignments: Quiz 4 (Interview #2), Bonus Visualization Reports (1% each, up to 4%)

[No Final Exam! Enjoy your break!]

Department Policies

Accommodations for students with disabilities/ADA: Northeastern is fully committed to creating a community characterized by inclusion and diversity. As part of this commitment, it upholds the American with Disabilities Act as Amended of 2008 and the American with Disabilities Act and Section 504 of Rehabilitation Act, referred to collectively as the ADA. The ADA requires Northeastern to provide reasonable accommodations to students with disabilities unless doing so would create an undue hardship, compromise the health and safety of members of the university community, or fundamentally alter the nature of the university’s employment mission. Students seeking information regarding ADA accommodations should review the University’s ADA Information and Resources Procedure available here.

Academic Integrity Statement: The Department of Political Science takes very seriously the issue of academic honesty, and as set forth in Northeastern University’s principles on Academic Honesty and Integrity Policy. The complete text can be found at NEU’s Office of Student Conduct and Conflict Resolution. Any student who appears to violate these principles will fail the course and will be put on academic probation. Individual faculty, with the support of the Department, can impose harsher penalties and as they deem necessary. Cheating is one example of academic dishonesty, and which is defined as using or attempting to use unauthorized materials, information, or study aids in any academic exercise. When completing any academic assignment, a student shall rely on his or her own mastery of the subject. Cheating includes plagiarism, which is defined as using as one’s own the words, ideas, data, code, or other original academic material of another without providing proper citation or attribution. Plagiarism can apply to any assignment, either final or drafted copies, and it can occur either accidentally or deliberately. Claiming that one has “forgotten” to document ideas or material taken from another source does not exempt one from plagiarizing. Your instructor will clarify specific guidelines on fair use of material for this class.

Title IX: Northeastern is committed to providing equal opportunity to its students and employees, and to eliminating discrimination when it occurs. In furtherance of this commitment, the University strictly prohibits discrimination or harassment on the basis of race, color, religion, religious creed, genetic information, sex, gender identity, sexual orientation, age, national origin, ancestry, veteran, or disability status. The Northeastern University Title IX policy articulates how the University will respond to reported allegations of sexual harassment involving students, including sexual assault, and provides a consolidated statement of the rights and responsibilities under University policies and Title IX, as amended by the Violence Against Women Reauthorization Act of 2013.

Syllabus: Research Methods


POLS 2399: Research Methods in Political Science

Mondays & Wednesdays 1:30 PM - 5:00 PM

(Virtual but Synchronized! Virtual attendance required!)

Instructor: Timothy Fraser, PhD Candidate in Political Science

  • Email:

  • Virtual Office Hours: Sign up on YouCanBook Me here

  • (Monday, Wednesdays, & Fridays 10 am - 12pm)

Course Description:

“Examines the range of research methods and designs used in political science, based on applying the logic of social scientific inquiry. Reviews experimental research, comparative methods, case studies, interviewing, surveys, program evaluation, and other topics relevant to the discipline, as well as questions related to the practice of research ethics. Course activities include intensive writing assignments by students. Requires prior completion of at least two of the following courses: POLS 1150, POLS 1155, and POLS 1160.”

Course Rationale:

Research methods like interviewing, surveying, experiments, mapping, and more are increasingly central to work in politics, business, and nonprofits. This course introduces the most common research methods in use today. Starting with zero prior knowledge, students will learn to interpret social science research and conduct their own studies!

Course Aims and Outcomes:

  • Develop and apply research methods skills related to the study of political science
  • Learn to read, write, speak about, and interpret costs and benefits of research designs
  • Practice using 9 different research methods in small groups and class activities
  • Learn about research ethics and gain certification in research with human subjects
  • Design your own proposal for a valid mixed-methods study

Course Structure:

  • Classes: Virtual-but-in-person. Attendance required, with video cameras on.
  • Course Website: Canvas - bookmark this website, and plan on using it everyday.
  • Course Zoom Link: https://northeastern.zoom.us/j/95178515423 (also on Canvas)
  • Readings: Free, on Canvas!

Grades and Assignments (in brief):

  • 4 responses (20%)
  • 2 group lab reports (20%)
  • 2 oral exams (30%)
  • 1 research proposal (20%)
  • Participation/Attendance (10%)

Course Philosophy

Your success in this class is important to me! We all learn differently. If there are aspects of this course that prevent you from learning or exclude you, please let me know as soon as possible. Together we’ll develop strategies to meet both your needs and the requirements of the course.


You will learn some basic research methods skills, including research design, interviewing, surveying, etc. (exciting!) This course is designed to support you in learning these new skills via three structures: office hours, small group, and group labs.


Students should regularly come to office hours. If you have questions about what we learn one day, come to office hours that week, because we will build on this knowledge in the next day.


You are welcome in this class. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with differences of race, color, culture, religion, creed, politics, veteran’s status, sexual orientation, gender, gender identity and gender expression, age, disability, and nationalities. Sometimes key information hasn’t reached me, though: Class rosters are provided to the instructor with the student’s legal name. I will gladly honor your request to address you by your preferred name and gender pronouns. Please advise me of this preference early in the semester so that I may make appropriate changes to my records.


3.5 hours of class is a lot of class. I promise to bring 150% enthusiasm, timely and relevant examples and readings, group workshops, frequent ‘labs’ where we can practice interviewing and surveying each other, peer review where your colleagues will review each other’s work and bolster it, breaks to help us get recharge, and exciting guest speakers to talk with about doing real social science out in the world.


In exchange, I need to ask of you:

  • Bring your enthusiasm, be present (keep your video on), stay engaged!
  • Get to know your fellow students! We will work together in small groups via breakout rooms every day.
  • Be kind and courteous, and support each other!
  • Come prepared! Do the readings before classes, and come prepared with questions, especially for our guest speakers!

Trust & the Honor Code

Communities, democracies, and especially classrooms are built on trust. I know that you and I are putting in a lot of effort to work through this pandemic, and it is draining beyond what we show. Through this class, we are going to build trust in each other - trust that I am here to support your learning, that your group-mates are there to support you, and that you are here to help your class get through this too.


One way you can do this is to always tell the truth. Each of you have pledged to do this when you joined Northeastern. Northeastern’s Honor Code reads: “On my honor, I pledge to uphold the values of honesty, integrity, and respect that are expected of me as a Northeastern student.”


This means no plagiarism - that means copying answers, finding answers online, passing off someone else’s work as your own, etc. Resources like Chegg are strictly off-limits too.


Why no plagiarism? The short answer is it impedes your learning, and makes both our lives awful. I will give you a zero on the assignment and report it to the university, every time, as I am required to. Also, I’m really good at catching it. But the longer, better answer is that honesty and trust are vital to being a good friend, family member, coworker, and member of a community, and after everything that has happened since 2020, we just owe that honesty and trust to each other.


So how do we practice it? Every time you complete an assignment, I would like you to write at the very top: “I have neither given nor received unauthorized aid on this assignment. Signed: [Your Name Here].” (Only sign it with your name if it is true!) I will only grade your assignment if you have signed this pledge, because it means you give me your word. In exchange, I will always assume you have told the truth in this class. It’s that simple.

How do I sign up for Office Hours?

Remote learning requires some self-teaching on your part, and that can be challenging. To help with this, I will be holding virtual office hours during the times listed on my calendar here.

  • These office hours will be one-on-one, and you can book them in 20 minute intervals.
  • You may book multiple time slots, but please be considerate of your classmates who may also wish to speak with me.
  • When using virtual office hours, please follow some basic decorum. For instance, please be fully clothed and please do not call from in bed.
  • You may book office hours with me at the following url: https://timothyfraser.youcanbook.me
  • All virtual office hours will be conducted on our same class Zoom link: https://northeastern.zoom.us/j/95178515423

Grades and Assignments (in detail):

4 Reading Reflection Papers (20%; 5% each)

Every other week, students will write up a short, 2-page double spaced response about the readings. Responses should cite and discuss (frequently) the readings, using proper in-text citations, responding to the questions below. No title page or references page required.

  • Wed. May 19, 1:30 PM: Should political scientists primarily use quantitative methods or qualitative methods? Why?
  • Wed. May 26, 1:30 PM: Should all political science studies use experiments? Why or why not?
  • Wed. June 16, 1:30 PM: What role do natural experiments and mapping have in political science?
  • Wed. Jun 23, 1:30 PM: What role do social network analysis and big data have in political science?


2 group lab reports (20%, 10% each)

Throughout term, your small groups still conduct many short research labs, where we will try out research techniques. Your groups will choose two of these labs, and you will write up their results of your analysis in a 2 page, double spaced paper. Lab reports should include (1) a short methods section introducing your (1a) research question, (1b) hypothesis, and (1c) how you will test them, (2) a results section summarizing results for each hypothesis, and (3) a discussion, summarizing (3a) the significance of your results, (3b) any limitations (what went well, what went less well), and (3c) what you would change of you did the lab again. Cite works from in class, but no references page required unless outside sources are used.

  • Due Friday June 11 at Midnight (pick from labs 1-4)
  • Due Friday June 25 at Midnight (pick from labs 5-8)


2 oral exams (30%, 15% each)

Two times in term, students will sign up for a 15 minute slot with me and respond to 3 questions. When responding students will draw cumulatively on their learning in class, refer to key readings, concepts, and terms, and teach me research design as if I were hearing about it for the first time.

  • Oral Exam #1 (Schedule (and complete) an exam via YouCanBookMe between Tuesday May 25 and Friday May 28. Sign up early to get your preferred time.
  • Oral Exam #2 (Schedule (and complete) an exam via YouCanBookMe between Monday June 28 and Wednesday June 30. Sign up early to get your preferred time.


1 research proposal (20%)

For their final paper, students will write up a 8-10 page research proposal, due Monday June 28 at midnight.

The final paper includes (1) a short, 1-page introduction clearly staying the research question and rationale for the study; (2) a brief, 3-page literature review clearly summarizing past research on each alternative hypothesis, followed by your hypothesis, drawing from at least 10 scholarly works; (3) a 1 page section on data and case selection; and (4) a 3-6 page methods section describing specifically how you would test your hypothesis against alternative hypotheses. This section must combine 2 methods discussed in class, drawing on key readings from class. One method must be drawn from the first half of class (Interviews, Indices, Surveys, Experiments), and one from the second half (Content/Text Analysis, Natural Experiments, Mapping, Big Data, Social Networks).

10% of this grade (2% overall) will come from a 1-page short proposal, describing (1) the research question, (2) alternative hypotheses, (3) your hypothesis, your sample/case selection (4), and (5) the two methods you plan on using, including how and why. Due Friday June 4 at midnight.


Participation/Attendance (10%)

Students are expected to be in class everyday, with their zoom video cameras turned on (unless specifically given permission to do otherwise). This is not to be draconian; Instead, this is to ensure that we can be together, build classroom ties, and close ties within your small group. I will take attendance everyday; to qualify, students must be (1) present, (2) with cameras on, and (3) contributing in their groups. Several times throughout term, I will assign you to come to office hours or review your classmates’ papers. These tasks will also come out of this grade, as listed on CANVAS.


Weekly Summary

Week 1

Monday, May 10: Research Design

  • Learning the Course
  • Research Design: Outcomes, Explanatory Variables, & Alternative Explanations
  • Seeking Truth: Positivism, Interpretivism, & Neo-Positivism
  • Readings:
    • Excerpt from: Gary King, Robert Keohane, and Sidney Verba (1994). The Science in Social Science. In Patrick H. O’Neil and Ronald Rogowski, Essential Readings in Comparative Politics (New York: W.W.Norton).

Wednesday, May 12: Qualitative Methods

  • Research Ethics (Complete Northeastern’s Online CITI Training before class)
  • Structured, Semi-structured, and unstructured interviews
  • Guest Lecture: Qualitative Methods
  • Lab #1: Qualitative Interviewing
  • Readings:
    • Daniel P. Aldrich. (2009). The 800-Pound Gaijin in the Room: Strategies and Tactics for Conducting Fieldwork in Japan and Abroad. Political Science and Politics 42(2), 299-303.
    • Layna Mosley, (2013). Just Talk to People? Interviews in Contemporary Political Science.In Mosley L. (Ed.), Interview Research in Political Science (pp. 1-28). Ithaca, NY: Cornell University Press.
    • Eric Bleich & Robert Pekkanen. (2013). How to Report Interview Data. In Mosley L. (Ed.), Interview Research in Political Science (pp. 84-106). Ithaca, NY: Cornell University Press.
    • Vanessa Williamson (2016). On the Ethics of Crowdsourced Research, PS: Political Science and Politics 49(1), 77-81.

Assignments - Wed. May 12 1:30 PM: Complete Northeastern’s Online CITI Training and upload certificate to Canvas

Week 2

Monday, May 17: Measurement and Case Selection

  • Case Selection & Sampling
  • Measurement & Validity (Interal, External, Conceptual)
  • Indices (Examples Polity V, HDI, Social Equity, SVI, SCI)
  • Guest Lecture: Making an Index
  • Lab #2: Making your own index with publicly available data (eg. census)
  • Readings:
    • Robert Adcock and David Collier. (2001). Measurement Validity: A Shared Standard for Qualitative and Quantitative Research. American Political Science Review 95 (3): pp. 529-546.
    • Pamela Paxton. (1999). Is social capital declining in the United States? A multiple indicator assessment. American Journal of Sociology 105: 88-127.
    • Dean Kyne & Daniel P. Aldrich. (2020). Capturing Bonding, Bridging, and Linking Social Capital through Publicly Available Data. Risks, Hazards, & Crisis in Public Policy 11(1), 61-86.

Wednesday, May 19: Surveys

  • Survey Sampling Design
  • Choosing Questions
  • Guest Lecture: Using Survey Data / Running your own Survey
  • Lab #3: Surveying with Qualtrics
  • Readings:
    • Video: An Introduction to Survey Questionnaire Design (Stacey Giroux)
    • Nora Cate Schaeffer and Stanley Presser. 2003. “The Science of Asking Questions.” Annual Review of Sociology 29: 65-88.
    • Mutz, Diana C. (2016). Harry Potter and the Deathly Donald. PS: Political Science & Politics, 49(4), 722-729. doi:10.1017/S1049096516001633

Assignments - Wed. May 19 1:30 PM: Submit Response #1 to Canvas

Week 3

Monday, May 24: Experimental Methods

  • Randomized Control Experiments (eg. Vaccine trials)
  • Field Experiments
  • Lab #4: Reviewing Experiments!
  • Reading:
    • David W. Nickerson. (2008). Is Voting Contagious? Evidence from Two Field Experiments. American Political Science Review, 102(1), 49-57.
    • Costas Panagopoulos. (2011). Thank You for Voting: Gratitude Expression and Voter Mobilization. Journal of Politics 73 (3): 707-717.
    • Diane Mutz. (2011). Treatments to Improve Measurement. In Population-Based Survey Experiments (pp. 1-22). Princeton: Princeton University Press.
    • Melissa Sands. (2017). Exposure to Inequality Affects Support for Redistribution. Proceedings of the National Academy of Sciences of the United States of America 114(4), 663-668.

Wednesday, May 26

  • Mixed Methods:
  • Case Selection Strategies
  • Guest Lecture #4 (Panel): Choosing the Right Mix of Methods for Your Research
  • Readings:
    • Evan Lieberman. (2005). Nested Analysis as a Mixed-Method Strategy for Comparative Research. American Political Science Review 99 (3): 435-452

Assignments - Monday May 24 1:30 PM: Peer Review #1. - Wed. May 26 1:30 PM: Submit Response #2 to Canvas - Oral Exam #1 (Schedule (and complete) an exam via YouCanBookMe between Tuesday and Friday. Sign up early to get your preferred time.

Week 4

Monday, May 31 [Memorial Day - No class]

Wednesday, Jun 2

  • Writing Workshop
  • Guest Student Research Panel: What did you research, how, and why?
  • Readings:
    • Jeffrey Knopf. (2006). Doing a Literature Review. PS: Political Science and Politics 39(1), 127- 132.
    • John Gerring et al. (2005). General advice on Social Science Writing.

Assignments - Wed. June 2 1:30 PM: Peer Review #2 - Friday, Jun 4 by Midnight EST: Submit Final Paper Proposal (1-page) to Canvas

Week 5

Monday, Jun 7 - [Free]

Wednesday, Jun 9 - Content Analysis - [Guest Lecture on Content Analysis] Text Analysis - [Guest Lecture on Text Analysis] - Lab #5: Content Analysis - Readings + Gary King, Jennifer Pan, and Margaret E. Roberts, (2013). How Censorship in China Allows Government Criticism but Silences Collective Expression,” American Political Science Review, 2013 + Annice Kim, Shiriki Kumanyika, Daniel Shive, et al.,“Coverage and Framing of Racial and Ethnic Health Disparities in US Newspapers 1996-2005.” American Journal of Public Health 2010

Assignments - Wed. June 9 1:30 PM: Peer Review #3 Fri. Jun 11 at midnight: Group Lab Report #1

Week 6

Monday, Jun 14: Natural Experiments & Quasi Experiments

  • Quasi-Experiments and balance
  • Natural Experiments
  • Small groups
  • Guest Lecture: Natural Experiments
  • Lab #6: Make your own matching experiment!
  • Readings
    • Thad Dunning (2012). Introduction. Natural Experiments in the Social Sciences. A Design-Based Approach. New York: Cambridge University Press.
    • Leah Stokes. (2015). Electoral Backlash against Climate Policy: A Natural Experiment on Retrospective Voting and Local Resistance to Public Policy. American Journal of Political Science 60(4), 958-974.
    • David J. Harding, Jeffrey D. Morenoff, Anh P. Nguyen , Shawn D. Bushway, and Ingrid A. Binswanger. (2020). A natural experiment study on the effects of imprisonment on violence in the community. Nature Human Behavior 3, 671-677.

Wednesday, Jun 16: Mapping & Geographic Information Systems

  • Spatial Patterns and Inequalities
  • Health Geography
  • [Guest lecture on GIS]
  • Lab #7: Mapping Social Infrastructure
  • Readings:
    • Alice Park, et al. (2021). An Extremely Detailed Map of the 2020 Election. New York Times.
    • Courtney Page-Tan (2021). The Role of Social Media in Disaster Recovery Following Hurricane Harvey. Journal of Homeland Security and Emergency Management 18(1), 93-123.
    • Masataka Harada, Gaku Ito, & Daniel M. Smith. (2020). Destruction from Above: Long-Term Legacies of the Tokyo Air Raids. Social Science Research Network.
    • Feldhoff, Thomas. (2017). Japan’s electoral geography and agricultural policy making: The rural vote and prevailing issues of proportional misrepresentation. Journal of Rural Studies 55, 131-142.

Assignments: - Mon. Jun 14 by 1:30 PM EST: Peer Review #4 - Wed. Jun 16 by 1:30 PM EST: Submit Response #3 to Canvas

Week 7

Monday, Jun 21: Big Data

  • Ethics in Big Data
  • Representativeness
  • Facebook Data for Good
  • Guest Lecture: Measuring Evacuation with Facebook Data
  • Readings:
    • Pierson, E., Simoiu, C., Overgoor, J. et al. A large-scale analysis of racial disparities in police stops across the United States. Nature Human Behavior 4, 736–745 (2020).
    • Maas, Paige, Shankar Iyer, Andreas Gros, Wonhee Park, Laura McGorman, Chaya Nayak, and P. Alex Dow. (2019). Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery. Proceedings of the 16th ISCRAM Conference – València, Spain May 2019.

Wednesday, Jun 23: Social Network Analysis

  • What’s in a network?
  • Network Concepts: Homophily, Clustering, Centrality, Contagion, Bipartite networks
  • Guest Lecture: Social Network Analysis
  • Lab #8: Network Mapping
  • Readings:
    • David Lazer. (2011). Networks in Political Science: Back to the Future. Political Science & Politics 44(1), 61-68.
    • Michael Dreiling et al. (2017). After the Meltdown: Explaining the Silence of Japanese Environmental Organizations on the Fukushima Nuclear Crisis. Social Problems 64, 86-105.
    • Nicholas Christakis, & James Fowler. (2007). The Spread of Obesity in a Large Social Network over 32 Years. New England Journal of Medicine 357, 370-379.

Assignments: - Wed. June 23 at 1:30 PM: Submit Response #4 to Canvas - Friday Jun 25 at midnight: Group Lab Report #2, due Friday at Midnight

Week 8: Final Exam Week

Monday, Jun 28 (no class)

Assignments: - Monday June 28 by Midnight: Submit Final Research Proposal to Canvas - Oral Exam #2 (Schedule (and complete) an exam via YouCanBookMe between Monday and Wednesday. Sign up early to get your preferred time.

Accommodations for students with disabilities/ADA

Northeastern is fully committed to creating a community characterized by inclusion and diversity. As part of this commitment, it upholds the American with Disabilities Act as Amended of 2008 and the American with Disabilities Act and Section 504 of Rehabilitation Act, referred to collectively as the ADA. The ADA requires Northeastern to provide reasonable accommodations to students with disabilities unless doing so would create an undue hardship, compromise the health and safety of members of the university community, or fundamentally alter the nature of the university’s employment mission. Students seeking information regarding ADA accommodations should review the University’s ADA Information and Resources Procedure available here.

Academic Integrity Statement

The Department of Political Science takes very seriously the issue of academic honesty, and as set forth in Northeastern University’s principles on Academic Honesty and Integrity Policy. The complete text can be found at NEU’s Office of Student Conduct and Conflict Resolution. Any student who appears to violate these principles will fail the course and will be put on academic probation. Individual faculty, with the support of the Department, can impose harsher penalties and as they deem necessary. Cheating is one example of academic dishonesty, and which is defined as using or attempting to use unauthorized materials, information, or study aids in any academic exercise. When completing any academic assignment, a student shall rely on his or her own mastery of the subject. Cheating includes plagiarism, which is defined as using as one’s own the words, ideas, data, code, or other original academic material of another without providing proper citation or attribution. Plagiarism can apply to any assignment, either final or drafted copies, and it can occur either accidentally or deliberately. Claiming that one has “forgotten” to document ideas or material taken from another source does not exempt one from plagiarizing. Your instructor will clarify specific guidelines on fair use of material for this class.

Title IX

Northeastern is committed to providing equal opportunity to its students and employees, and to eliminating discrimination when it occurs. In furtherance of this commitment, the University strictly prohibits discrimi- nation or harassment on the basis of race, color, religion, religious creed, genetic information, sex, gender identity, sexual orientation, age, national origin, ancestry, veteran, or disability status. The Northeastern University Title IX policy articulates how the University will respond to reported allegations of sexual harassment involving students, including sexual assault, and provides a consolidated statement of the rights and responsibilities under University policies and Title IX, as amended by the Violence Against Women Reauthorization Act of 2013.