Skills for Effective Interviewing
Instructor: Sue Weare
Date: Monday July 24
​Location: BA 112
  • Interviewing is the most common method of data collection used in qualitative research. This 1-day course provides a detailed overview of individual and group interviews, which are the most common methods of data collection used in qualitative research. By the end of this session, you will be familiar with benefits and challenges of interviewing, how to create an interview protocol, how to craft effective questions, key skills in conducting the interview, and how to assess the quality of an interview. This is a hands-on course; you will have opportunity to practice your new knowledge and skill through role-playing in small groups. This workshop is appropriate for academics and professionals alike.
  • Pre-requisites: None.

Introduction to Nvivo
Instructor: Ellis
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.

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 qualitative data. This course offers an overview of concepts, approaches, and strategies in QDA, with a focus on analysing data from interviews. It provides a comprehensive introduction to thematic analysis, a method that is broadly applicable. By the end of this course, you have a better understanding of how to plan for QDA, be familiar with basic coding techniques, know how to ensure your findings are trustworthy, and be ready to start analysing qualitative data. This is a hands-on course; you will have opportunity to practice your new knowledge and skill by working with tutorial data. This workshop is appropriate for academics and professionals alike.
  • Pre-requisites: None.

Realist Review
Instructor:  Ketan Shankardass
Course Date: Thursday July 21
​Location: BA110
  • Realist review is an emerging methodology for knowledge synthesis appropriate for complex interventions. Whereas more traditional methods of systematic review have been used effectively to synthesize evidence about relatively simple relationships, realist review is a complimentary method suitable for understanding which interventions work, for whom and why, and under what circumstances. This 1-day session will: provide a background of the scientific realist perspective; describe methodologies and examples of different approaches to realist review, including analogues to systematic reviews and scoping reviews; critically discuss the strengths and weaknesses of a realist approach; and provide an opportunity to help you apply this approach to your own data or research question.

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

Knowledge Translation
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

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

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. 

Introduction to Qualtrics
Instructor: Bianca Dreyer
Course Date: July 19
  • This one-day interactive workshop is ideal for anyone interested in collecting quality quantitative and qualitative survey data, without the hassle of data entry. This workshop introduces both basic and advanced features of Qualtrics, a powerful survey tool that allows users to build complex online surveys. The first half of the day will cover basic features, such as creating and managing surveys, organizing account libraries and adding and editing questions. The second half will cover more advanced features, such as advanced question options, loop and merge, skip logic, display logic, carry-forward, block options and survey flow. Participants will receive hands-on experience working through example surveys and in-class projects. By the end of the workshop, participants will be able to navigate Qualtrics, create new surveys, add a variety of different question types, and edit the survey flow as needed.
  • Pre-requisites: None

Choosing Among 5 Qualitative Reseach Approaches
Instructor: Cheryl Poth
Course Date: Thursday, July 19 
  • Have you wondered how to determine the best fit of a qualitative research approach with the study purpose for your research idea? Together, in this half day session, we will explore the designs and procedures inherent to five qualitative research approaches: narrative research, phenomenology, grounded theory, ethnography, and case study. The session will be organized around four key questions: What are the origins and defining features of each approach, what types and methods are associated with each approach, what data analysis and writing structures are commonly used for each approach, and what challenges and ethical considerations are likely encountered for each approach? Participants are encouraged to bring their ideas for a qualitative study to explore during the workshop as there will be embedded opportunities for small and large group discussions. No prerequisites but some understanding of the fundamentals of qualitative research is assumed. Participants may wish to explore the following reference as background reading for the session: Creswell, J., & Poth, C. (2017). Qualitative inquiry & research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage..
  • Prerequisites:Some understanding or previous experiences with qualitative research is an asset.

Statistical/Machine Learning
Instructor: Dave Armstrong
Course Date: July 25 - 27
  • The 'big data' revolution has required us to rethink not only how we conceive of and collect data, but also the methods we use to analyze our data. In this workshop, we will put the parametric statistical models with which we are all familiar (e.g., OLS, logit, probit) in the context of this larger discussion about statistical learning. These ubiquitous models are simple cases of statistical learning algorithims that can be extended in lots of interesting directions. In the course, we will discuss both supervised learning (models that have a dependent variable, like those for regression and classification) and unsupervised learning (models without a dependent variable, like principal components analysis, and clustering). In particular, we will briefly cover OLS regression and GLMs as simple regression/classification tools. We will then turn to extensions of these models with automatic variable selection (ridge regression and the lasso). Next, we will then branch out to cover semi- and non-parametric alternatives, like generalized additive models (GAMs), kernel regularized least squares, multivariate adaptive regression splines and tree-based regression models. Finally, we will cover clustering, principal components analysis and some extensions like finite mixture models. The course will have both theoretical and applied aspects, though it tends to focus more on applications. All of the models mentioned above have straightforward implementation in R, which we will use thorughout the course. The course will have a lecture component, structured labs and more flexible time where you can try out what you're learning on your own data. Screen reader support enabled. .
  • Prerequisites:Familiarity with parametric statistics

Digital Scraping
Instructor: Tom Cardoso
Course Date: Friday July 20 
  • Introduction to Digital Scraping: One of the most time-consuming aspects of performing any sort of data analysis is getting that data in the first place. Often, a straightforward, well-structured database doesn't exist, which means you need to build one yourself, from scratch. That's where scraping comes in: you can build a program to automate this collection for you, saving countless hours of boring and imprecise data entry. In this one-day class, you'll learn how to decide on the structure for your data, pick the right scraping approach, create a scraper and systematize your data collection. The class will introduce the basic concepts and strategies behind scraping, and focus on getting data off both websites and offline documents (such as PDFs)..
  • Prerequisites:Basic knowledge and familiarity with R, Python or JavaScript.

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.