Evidence-Based Course Evaluation with Q-Methodology
Workshop W4. QM4CE
How do you use your annual course evaluations to revise your course for the coming year? With Likert-scale rankings, and a few qualitative comments, your options are limited and many instructors struggle to find what works best for the largest number of students. If this sounds like you, Q-methodology might be what you’re missing. In this workshop, we’ll explore the use of Q-methodology – a technique where qualitative data are analyzed using quantitative techniques for course evaluation.
In this workshop participants will learn the fundamentals of Q-methodology and then work through practically applying their understanding to each of the three phases involved: 1) instrument development, 2) data collection and 3) analysis and application. In the end, you’ll leave with an understanding of the whole process and a revitalized outlook on how evidence-based course evaluations can allow you to make targeted changes that you know will impact multiple learners – and not cater to the disgruntled few.
Objectives
By the end of this workshop, participants will be able to:
- Compare/contrast Q-methodology findings with traditional Likert results outlining a practical potential for course evaluation to lead to course evolution.
- Outline how a Q-methodology study is conducted,
- Participate in the creation, application and interpretation of a Q-methodology study, and
- Develop strategies for employing Q-methodology in their own educational context.
Reference
Brewer-Deluce D, Sharma B, Akhtar-Danesh N, Jackson T, Wainman BC. Beyond Average Information: How Q-Methodology Enhances Course Evaluations in Anatomy. Anat Sci Educ. 2019;13(2):137-148.
Organizers
Noori Akhtar-Danesh, PhD is an Associate Professor of Biostatistics in the School of Nursing and the Department of Health Research Methods, Evaluation & Impact (HEI) at McMaster University. He has over 20 years of experience teaching graduate-level statistics, providing statistical consultation, and analyzing large and complex datasets. Dr. Akhtar-Danesh’s methodological expertise includes survival analysis, multilevel modeling, longitudinal data analysis, and Q-methodology. He has published more than 30 peer-reviewed articles using Q-methodology and has developed two Stata programs for Q-methodology data analysis (Qfactor and Qpair).
Danielle Brewer-Deluce, PhD is an award-winning education scientist with nearly a decade of experience doing Q-methodology research. She is an assistant professor in the department of Pathology and Molecular Medicine, Faculty of Health Sciences at McMaster University where she teaches anatomy at undergraduate and professional levels. Her research program centers on understanding how students learn anatomy and what adjuncts would help them do better.
Together, our team has led multiple Q-methodology workshops at local, national and international conferences, supporting researchers from diverse disciplines in applying this approach to their work.
Intended Audience
This workshop is appropriate for anyone in higher education looking for a new way to evaluate their courses and receiving evidence-based, actionable feedback. A participant limit of approximately 20-24 is ideal to facilitate the small group work integral to this workshop.
Other Information
Participants should expect a workshop that combines didactic and active group work throughout to establish a working understanding of Q-methodology. They should come up with an appreciation for the strengths and weaknesses of their own course evaluations and some ideas for what they’d need to move through course reform more effectively.