University of Toronto Social Science Methods Week 2020

August 31 – September 2, 2020

The Social Science Methods Week (SSMW) consists of 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 at the University of Toronto. 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 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 15 participants to allow us to maintain appropriate social distancing (though this may be adjusted up or down in the future to comply with public health guidance).

Those wishing to be placed on a waitlist for a workshop should email their request to ssrmweek@utoronto.ca. Space permitting, same-day walk-ins will also be welcomed on a first come, first served basis.

Light refreshments will be provided for registered workshop participants.

Participants should bring a laptop to the workshops with any necessary software pre-installed.

All workshops will be held in Room 36 in the Department of Sociology, St. George Campus, located at 725 Spadina Ave, Toronto, ON M5S.

Questions can be directed to ssrmweek@utoronto.ca.

Note on COVID-19: Please note that offerings are more limited this year due to the disruptions caused by the COVID-19 pandemic. We will continue to monitor the situation and will adjust enrollment as needed to comply with government regulations and recommendations by health officials. If necessary, we will cancel SSMW and reschedule it for next spring.

 

Registration (to follow)

 

Schedule of Workshops


 Monday, August 31Tuesday, September 1Wednesday, September 2
Morning9 am - 12 pm
Introduction to R, the Tidyverse Way
9 am - 12:30 pm
Introduction to Social Network Analysis
9 am - 12 pm
Improving Representation of Non-representative Samples: Multi-level regression with Post-stratification
Lunch Break12 pm - 1 pm12:30 pm - 1 pm12 pm - 1 pm
Afternoon1 pm - 4 pm
Hearing Silence in Qualitative Interviews
1 pm - 5 pm
Introduction to Python
1 pm - 4 pm
Survey Experiments: Theoretical and Practical Considerations


Workshop Descriptions

Title: 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/


Title: 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.


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.


Title: 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.


Title: Improving Representation of Non-representative Samples: Multi-level regression with Post-stratification
Length: 3 hours
Instructor: Rohan Alexander, Information 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).


Title: 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 behavior 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.