University of Toronto Social Science Methods Week 2021


April 26 – April 30, 2021

The Social Science Methods Week (SSMW) is a series of workshops on methods with high potential to improve and advance research in the social sciences. Workshops are taught by expert methodologists drawn from departments across the social sciences. SSMW is an interdisciplinary forum where researchers can upgrade their methodological toolkit and build cross-disciplinary ties to like-minded scholars.

Registration is free, and open to graduate students, post-docs, staff, and faculty at the University of Toronto. Participants can register for as many workshops as they wish, though enrollment for each workshop is capped at 30 participants. Those wishing to be placed on a waitlist for a workshop should email their request to ssrmweek@utoronto.ca.

Participants should have any necessary software pre-installed on their personal computers.

All woRegistration buttonrkshops will be held online using Zoom. Links for each session will be emailed to registered participants the week prior to the workshops.

Questions can be directed to ssrmweek@utoronto.ca.

Register for a workshop

Schedule of Workshops


 Monday, April 26Tuesday, April 27Wednesday, April 28Thursday, April 29Friday, April 30
Morning9 am - 12 pm
Introduction to R, the Tidyverse Way
9 am - 12 pm
Hearing Silence in Qualitative Interviews
10 am - 1 pm
Survey Experiments: Theoretical and Practical Considerations
9 am -12 pm
Introduction to Web Scraping and APIs with Python
9 am - 12:30 pm
Introduction to Social Network Analysis
Lunch Break12 pm - 1 pm12 pm - 1 pm1 pm - 1:30 pm12 pm - 1 pm
Afternoon1 pm – 4 pm
The Practice and Ethics of Community Engaged Research
1 pm – 5 pm
Introduction to Python
1:30 pm – 4:30 pm
Improving Representation of Non-representative Samples: Multi-level Regression with Post-stratification
1 pm – 3:30 pm
Leveraging Time Use Data in Social Science Research


Workshop Descriptions

Introduction to R, the Tidyverse Way
Length: 3 hours
Instructor: Sue Song, Psychology

R is a powerful statistical software package that can make your research more reproducible and efficient. In this workshop, we will work through cleaning and manipulating your data using the Tidyverse, a collection of R packages designed for data science. No prior experience with R or the Tidyverse is required for the workshop, but the attendees should have the latest versions of R and RStudio installed.

R: http://cran.utstat.utoronto.ca/

RStudio: https://rstudio.com/products/rstudio/download/


The Practice and Ethics of Community Engaged Research
Length: 3 hours
Instructors: Sharla Alegria, Sociology and Judith Taylor, Sociology

This workshop will feature a question-and-answer style conversation between a veteran community engaged researcher, Judith Taylor, and conscientious abstainer from community engaged research, Sharla Alegria. It will provide resources, opportunities for feedback and discussion of research ideas, and a rich exploration of the practical, ethical, and epistemological promises and dilemmas of community engaged research. There are no prerequisites or software requirements for this workshop.


Hearing Silence in Qualitative Interviews
Length: 3 hours
Instructor:Ping-Chun Hsiung, Sociology

Researchers conducting qualitative interviews tend to focus on the words and stories uttered by the informants. Insufficient attention has been directed to the unspoken and/or unspeakable. This workshop fills this gap by conceptualizing “silence” as an interactive encounter co-constructed by the researcher and informant in qualitative interviews. Using interview excerpts, this workshop illustrates how to make silence audible and the implications of hearing silence in qualitative interviews. It also tackles broader issues pertinent to the politics of knowledge production in social science inquiry.


Introduction to Python
Length: 4 hours
Instructor: Catherine Yeh, Sociology

Python is a programming language. In the hands of social scientists, Python is a powerful tool for collecting, analyzing, and visualizing all kinds of data. This Intro to Python workshop is for both qualitative and quantitative researchers who have little or no experience with Python or any other programming language. We will introduce the basic elements of Python like syntax, data types, objects, and functions. We will learn how to get data into and out of Python by reading and writing files. We will learn how to access and manipulate data. Most importantly, we will introduce the active and exceptionally helpful online Python community, so that once the workshop is over everyone will be able to get good answers on their own to questions about how to do stuff in Python. By the end of this workshop, everyone will get this “joke”: Real programmers count from 0. Please install Python (version 2.7 or 3) on your computer before the workshop. A popular way to do this is to install Anaconda, which includes the spyder application for using Python.


Survey Experiments: Theoretical and Practical Considerations
Length: 3 hours
Instructor: Michael J. Donnelly, Political Science

This is an introduction to the theory and practicalities of doing your own survey experiments. Survey experiments are a useful tool for people studying human attitudes and behaviour and give us the ability to overcome some common inferential challenges. The workshop is designed for graduate students and faculty with an interest in fielding their own experiments. It will cover the goal(s) of survey experiments, a brief overview of common designs, some key debates about design and analysis, and the practical aspects of designing, funding, piloting, work-shopping, piloting, piloting, contracting, piloting, and fielding survey experiments. There are no prerequisites, though some knowledge of survey research is assumed.


Improving Representation of Non-representative Samples: Multi-level regression with Post-stratification
Length: 3 hours
Instructor: Rohan Alexander, Information and Statistical Sciences

Non-representative samples are ubiquitous in social research. They include convenience samples, online panels, and crowdsourced research pools like Amazon’s Mechanical Turk. Even nationally representative samples may not be representative when they are disaggregated, say, to a province-level. Multi-level regression with post-stratification (MRP) is a popular way to adjust non-representative samples so that their responses better represent a given population. MRP uses a regression model to relate individual-level survey responses to various characteristics and then rebuilds the sample to better match the population. In this way, MRP allows researchers to better understand the bias in their data, go some way to adjusting for it, and more confidently generalize their results beyond particular samples. However, it can be a challenge to get started with MRP as the terminology and data requirements may be unfamiliar.

The purpose of this hands-on workshop is to de-mystify MRP and give participants the ability and confidence to:

  1. critically read papers that use it; and
  2. apply it in their own work

Examples of how to implement MRP will be illustrated in R; however handouts will be provided in other languages on request. Familiarity with R and multi-level models is helpful but not required. Workshop participants should bring a laptop that is: a) connected to the internet; and b) has R and R Studio installed, along with the “tidyverse” and “brms” packages (if you have a hassle doing this then come early to the workshop and I can help you).


Introduction to Web Scraping and APIs with Python
Length: 3 hours
Instructor: Fedor Dokshin, Department of Sociology

The Internet has opened new opportunities for social research. As an ever-expanding range of social activity moves online, websites become repositories of fine-grained and time-stamped records of human behaviour and interaction. Besides digital traces of online activity, the Internet also offers access to digitized datasets about the non-digital world. These data include, for example, massive amounts of digitized historical text, public documents, administrative datasets, and records of real-world events. This workshop will introduce participants to a few practical tools for collecting data from the Internet. After reviewing some of the possibilities that the Internet affords social scientists, we will walk through hands-on examples using Python to collect data from the Internet. We will cover the basics of working with application programming interfaces (APIs) and the rudiments of web scraping and web crawling. To follow along (which you’re encouraged to do), you will need to install Python on your computer (examples will use Python 3). Some basic familiarity with Python will help you get the most out of this workshop. If you have no experience with Python, you are encouraged to enrol in the Intro to Python workshop.


Leveraging Time Use Data in Social Science Research
Length: 2.5 hours
Instructors: Melissa Milkie, Sociology; Dana Wray, Sociology

In this workshop, we introduce time use research, including the history and development of various national time diary collections in Canada, the U.S., and internationally. Scholars examine time use in research on social inequalities, health and well-being, paid and unpaid work, caregiving, and more. The workshop will present a hands-on introduction to analyzing time use data using the IPUMS Time Use Collection – which includes the American Time Use survey (ATUS), the American Heritage Time Use surveys (AHTUS), and the Multinational Time Use surveys (MTUS). Participants will learn the structure of time use data, become familiar with the breadth and depth of this type of data, and analyze this data using the IPUMS web-based extract system and STATA (or a software of their choice). The workshop will also introduce participants to the creative ways in which researchers exploit the richness of time use data, including sequence analysis, latent class analysis, and using time diaries as panel data.


Title: Introduction to Social Network Analysis
Length: 3.5 hours
Instructor: Chris Smith, Sociology

Social network analysis (SNA) is a method for investigating social structures through the use of network and graph theories. It is used across a wide range of disciplines, from biology to sociology. The techniques covered in this workshop are applicable to any number of data types and disciplines. This primer begins with social network data collection and relational data organization and then introduces basic visualization and analysis in the free statistical and graphical platform R. No background in social network analysis is required. Familiarity with the statistical package R is helpful but not required.