EVAL880
Data Analysis, Interpretation and Communicating Your Findings
Data collection for the purpose of program evaluation is an inherently political process. What data is collected, by whom, how it is interpreted, and how it is used (or not) affects policy, organizations and the people they serve. In this course, you will examine data analysis and interpretation, as well as ways to communicate your findings effectively and creatively for transformation and learning. You’ll also consider the notion of reciprocal accountability, deepening your understanding of how funders and others can share key learning back to the people or agencies collecting the data.
Through this course, you’ll critically explore how data has traditionally been used and interpreted, and how it can be better used to influence social change.
Overview
Location: Online
Format: Self-paced within deadlines set by instructor
Duration: 6 weeks
Tuition: $970–$995
Can be applied to:
Evaluation for Social Change and Transformational Learning Certificate
Upcoming Offerings
Register for a course at any time, with the option to apply to a program later.
- Wed, Apr 30 (self-paced all week)
- Wed, May 7 (self-paced all week)
- Wed, May 14 (self-paced all week)
- Wed, May 21 (self-paced all week)
- Wed, May 28 (self-paced all week)
- Wed, Jun 4 (self-paced all week)
Sofia Vitalis
What you will learn
After completing this course, you’ll be able to do the following:
- Define collaborative inquiry and its role in transformative change
- Analyze the strengths and challenges of standardized evaluation tools
- Use the Adaptation Framework to adapt data collection tools to support collaborative, consultative evaluation
- Assess the influence of traditional research paradigms that guide data analysis techniques
- Describe the phenomenological paradigm, social constructivist paradigm, transformative/critical theory paradigm and Indigenous research paradigms
- Describe the differences between positivism and transformative/critical paradigms and the strengths and weaknesses of each
- Identify the social science habits that undermine transformative evaluation and locate them in an analysis of an existing evaluation
- Create links between quantitative and qualitative data analysis approaches and findings by building on reflections of reciprocal accountability
- Examine critically the validity and reliability of quantitative and qualitative data
- Examine and test validity and reliability through storytelling
- Assess the level of objectivity of a data collection plan
- Cite examples of the political nature of evidence
- List the standards of evaluation and reciprocal accountability
- Identify common biases in data interpretation
- Apply an understanding of the influence of positionality, power and privilege to evaluation practice
- Explore two-eyed seeing and balancing worldviews in data interpretation
- Communicate findings in a range of effective and creative ways, including visual representations, social media platforms, and storytelling/narrative
- Choose and apply innovative and strengths-based ways to present “bad news” findings as learning opportunities
- Explain how data can be used for learning, transformation and social change
How you will learn and be evaluated
- Prepare to spend about 10 hours per week on coursework
- Plan for advance reading
- Expect reading and other assignments on a weekly basis
- Plan to access the course at least once every few days to keep up with your work and group exercises
You will be evaluated on:
- Class participation
- In-class assignments
- Post-class reflection assignment
Learning Materials
No textbook is required. We will provide all course materials online.
Technical Requirements
For online courses, you will need a computer with audio and microphone that is connected to the internet. Canvas is the online system that will be used for the course. For more information and online support, visit Online Learning.