Skills for Effective Interviewing
Instructor: Sue Weare
Date: Monday July 24
​Location: BA 112
  • Interviews and focus groups are the most common methods of data collection used in qualitative research. This 1-day course provides a detailed overview of both approaches: their benefits and challenges, how to prepare, effective types of questions, key skills involved, and how to create a protocol for the interview. By the end of this session, you will be familiar with the steps involved in planning for and conducting an effective qualitative interview or focus group. This is a hands-on course; you will have opportunity to practice your new knowledge and skill through role-play in small groups.
  • Pre-requisites: None.

Introduction to Nvivo
Instructor: Raymond McKie
Date: Friday, July 21
​Location: BA206
  • This 1-day workshop is suitable for people working with qualitative data. The first half of the course will cover basic usage of NVIVO software, organizing data and/or literature, basic coding, and methodological considerations. The afternoon portion of the course will cover intermediate-advanced usage of NVIVO such as, creating a codebook, querying/quantifying data, coding audio/video, organizing classifications, and data visualization. This course is taught using an interactive approach and attendees will be able to practice their skills using NVIVO. Individuals are encouraged to bring a working dataset or a selection of literature. If you do not have current data or relevant literature to review, a sample dataset and literature will also be provided. .
  • Pre-requisites: None.

Basic SPSS Usage
Instructor: Anthony Piscitelli
Course Date: Wednesday, July 19
Location: BA206
  • This 1-day workshop is ideal for anyone working, or planning to work, with survey data. No background in research methods or statistical analysis is required. This workshop introduces basic skills in data analysis using SPSS. The day will mix discussion of statistical analysis principles (e.g. variables, formulating hypotheses, levels of measurement, univariate statistics, bivariate statistics, etc.) with hands-on training. By the end of the workshop, participants will be able to import data into SPSS, recode variables, generate tables, and execute some basic descriptive analysis. 
  • Pre-requisites: None.

 The Comparative Method
Instructor: Zachary Spicer
Date: Tuesday July 25 (morning)
​Location: BA 111
  • Comparison is a fundamental tool of analysis. The comparative method not only allows researchers to analyze certain phenomenon, but also plays a central role in concept-formation and hypothesis testing, particularly when researchers have a small number of cases. This half-day workshop provides strategies for researchers hoping to undertake comparative research and offers best practices for case selection and analysis. 
  • Pre-requisites: None.

Introduction to Qualitative Analysis
Instructor: Sue Weare
Course Date: Tuesday July 25
Location: BA 112/em>
  • Qualitative data analysis (QDA) turns qualitative information into findings. It is the process through which we identify, explain, interpret and make sense of the data arising out of text-based data. In this course, you will be introduced to concepts, approachesand strategies in QDA with a specific focus on the analysis of interviews and focus groups. By the end of this course, you will have a better understanding of  what QDA entails, be familiar with basic approaches to coding, and know how to plan for the analysis of qualitative data.  This is a hands-on course; you will have opportunity to practice your new knowledge and skill by working with tutorial data.
  • Pre-requisites: None.

Arts-Based Methods
Instructor:  Ciann Wilson
Course Date: Tuesday July 18
​Location: BA 112
  • This interactive 1-day session will serve as an introduction to arts-based qualitative research. Arts-based methods are emerging approaches that involve the use of artistic processes as the mode of inquiry. The course will be structured around case studies and the knowledge and lived experiences of participants.  
  • Pre-requisites: Basic understanding of qualitative methods

Introduction to Research Data Management
Instructor:  Michael Steeleworthy
Course Date: Monday July 17
​Location: BA 111
  • Funding agencies, publishers, and scholars are today increasingly concerned with Research Data Management (RDM), the organization, security, access, and preservation of research data. RDM can encompass a research team’s access controls and backup procedures, their file and content descriptions, hardware and software requirements, privacy obligations, preservation strategies, and more. This information is vital to the proper stewardship of research data, and Canada’s funding agencies will require grant applicants to summarize their data management plans in the near future.  Participants in this session will learn about the TriCouncil policy drivers that are making RDM a critical part of their research planning. We will examine the components of a data management plan (DMP) and use free resources to write DMPs of our own. Participants will also be introduced to Dataverse, the data management service provided by the Ontario Council of University Libraries to use for their own projects. By the end of the session, participants will gain practical knowledge, resources, and advice to properly steward their data through the research lifecycle.
  • Participants are asked to bring their own laptop (Mac or PC) and be prepared to discuss data management strategies and outcomes for a current, recent, or future project.   
  • Pre-requisites: None.

Survey Design
Instructor:  Sean Simpson
Course Date: Wednesday July 26
Location: BA 111
  • Many people underestimate the art and science that goes into developing and designing a good survey. This 1-day course will help you develop surveys that will help your respondents to understand and answer the questions the way you intended it. The instructor will draw on several decades of psychological research on the survey response process as well as his own experience as a researcher, teacher and consultant in survey design. The course is structured along 10 core principles of good survey design and is easy to follow with no prior knowledge needed. Among others, topics include the cognitive-affective survey response process, the survey development process, survey structure and format, question wording, and online surveys. The course is conducted in an interactive way and participants will be able to apply their knowledge through practice exercises.
  • Pre-requisites: None

Sampling and Recruiting
Instructor: Sean Simpson
Course Date: Thursday July 27
Location: BA111
  • Theis 1-day workshop is designed for those interested in learning more about sampling and how to draw an ideal sample for surveys, geared towards professionals and practitioners who need to conduct or analyze survey-based research. The material is grounded in sampling theory (both probability and non-probability), the rationale and applicability of margins of error, and how to analyze surveys with a critical eye. The workshop also focuses on applying these theories and best-practices using case studies as examples across multiple methodologies and platforms: telephone interviewing (live and automated), online, paper-based, and intercepts among others. Practical elements such as feasibility, price, and optics will all be explored. Following the course, registrants will be comfortable both critiquing research that has been conducted based on its sampling methods, and designing sampling frames that are both theoretically sound and applicable to real-life situations.
  • Pre-requisites: None

Getting the right knowledge to the right people, at the right time, in the right format
Instructor:  Shawna Reibling
Course Date: Saturday July 22
Location: BA 112
  • Knowledge translation involves moving your research out of the “academic” realm and to those who can put the results of your research inquiries into action. This process begins when you begin your research and continues through the conclusion of the data analysis and production of findings. This course will take you through the knowledge translation planning process focused on social sciences and humanities based research topics.
  • Pre-requisites: None

Content Analysis
Instructor: Jennifer Long
Course Date: Friday July 21
Location: BA 112
  • Content analysis is used in both qualitative and quantitative research as a means to pull out important ideas and themes from one's data and to reveal information about the big picture. This method involves the labelling and coding of data in order to recognise the similarities and differences found within. Content analysis is typically used for written documents or recorded verbal communications, however; this systematic and objective method can be used on any item which can be made into text, for example, on photographs, videos, or art installations. This 1-day session will provide an overview of content analysis, its use in qualitative and quantitative research, methods for selecting and applying codes, and its strengths and weaknesses in various contexts. In addition, participants will receive hands-on experience working through case studies and in-class projects. This session will end with a brief discussion of available software applications that assist with this form of analysis.
  • Pre-requisites: Basic understanding of qualitative methods

Instructor: Nina Rosenbusch
Course Date: Monday July 17
Location: BA 112
  • Whenever theoretical arguments are contradictory and a large body of empirical research exists on a topic that shows inconclusive results a meta-analysis can shed more light on the true relationship between variables as it allows researchers to synthesize empirical research and to quantify effects. This interactive 1-day workshop will help participants to understand the method of meta-analysis. The course is structured along the different phases of a meta-analysis including the study location process, coding, bivariate analyses and multivariate analyses. Participants will apply their knowledge in hands-on exercises. 
  • Pre-requisites: None

Introduction to Analysis Using R
Instructor: Simon Kiss
Course Date: Tuesday July 18
Location: BA206
  • R is a powerful and open-source software package for statistical analysis that is, by some measures, becoming the standard for data analysis in the private, public and academic sectors.  It’s strengths are its open nature, flexibility, its capacity to produce compelling and informative graphics and the thousands of users that produce an astonishing array of packages that enable the software to perform statistical packages. This day-long session will introduce participants to R tailored to their topics of interest and level of familiarity with statistics and data analysis.  Topics will include basic data management strategies, producing descriptive and bivariate statistics, the linear and generalized linear model and data visualization.
  • Pre-requisites: An undergraduate course in statistical analysis

Instructor: Jennifer Long
Course Date: Thursday July 20
Location: BA 112
  • Ethnography is known as a qualitative research method often used by Cultural Anthropologists during year-long excursions to field sites all around the world. However, we'll be learning about those ethnographic techniques and approaches that have become popular in North American workplaces and are similar to the tools used by Applied Anthropologists and user experience researchers. Participants in this 1-day course will also be introduced to methods such as participant observation, ethnographic writing, ethnographic interviews, genealogical research, and auto-ethnography techniques. Application of these techniques can be used in your workplace or as a supplement to other qualitative and quantitative research methods.
  • Pre-requisites: Basic understanding of qualitative methods

Hierarchical (Multi-Level) Modelling
Instructor: Manuel Riemer
Course Date: July 26-28
Location: BA112
  • This 3-day advanced multivariate statistics course provides an introduction to applied analyses of multilevel models. Students will learn how to use multilevel models for analyzing clustered data (e.g., persons nested in groups) and longitudinal data, such as flexible strategies for modeling change and individual differences in change. Multilevel models are known by many synonyms (hierarchical linear models, general linear mixed models). The defining feature of these models is their capacity to provide quantification and prediction of random variance due to multiple sampling dimensions (across occasions, persons, or groups). Multilevel models are useful in analyzing clustered data (e.g., persons nested in groups), in which one wishes to examine predictors pertaining to individuals or to groups. Multilevel models also offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, the possibility of examining time-invariant or time-varying predictor effects, and the use of all available complete observations. This course will serve as an applied introduction to multilevel models for both longitudinal and clustered data, as well as combinations thereof. We build upon familiar regression models and expand into multilevel regression models from that starting point.
  • Prerequisites: A solid knowledge of multiple regression and basic knowledge of SPSS. 

Mainstream and Social Media Analysis
Instructor: Andrea Lawlor
Course Date: Monday July 24
Location: BA111
  • In this two-day course, students will learn how to collect, code, analyze and report on print, broadcast, and social media data. We begin with automated data collection using media databases and automated web scraping programs. Analysis techniques will include designing topic extraction models, manual and automated coding techniques, coding for tone and multidimensional scaling. The course also includes discussion of actor analysis, incorporating public opinion polling into media analysis, and the visualization of results. Programs used will include QDA Miner, WordStat and Lexicoder. 
  • Prerequisites: The course assumes a basic knowledge of data visualization and elementary statistics, but no prior knowledge of programming or software is required.  

Structural Equation Modelling
Instructor: Simon Coulombe
Course Date: July 20-21
Location: N1055
  • Structural equation modelling (SEM) is a statistical technique that encompasses multiple linear regression, path analysis, factor analysis and causal modelling with latent variables in a unified framework. Health and social scientists as well as market researchers can use SEM to create powerful models that explain various aspects of human behaviours, from well-being to social attitudes and consumer behaviour. This course covers the main background principles of SEM, preparing data for SEM, model-building strategies, and applications of SEM in health and social sciences. The course covers different techniques that are part of the SEM "family": path analysis, confirmatory factor analysis, structural regression models, and cross-lagged structural equation modelling. SEM analyses will be demonstrated in two popular SEM programs: Mplus (a command driven interface) and Amos (with a graphical user interface).
  • Pre-requisites: Basic familiarity with multiple linear regression and factor analysis is assumed in this course.