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 module, 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 module, you’ll critically explore how data has traditionally been used and interpreted, and how it can be better used to influence social change.

This course is available at the following time(s) and location(s):

Campus Session(s) Instructor(s) Cost Seats available  
Online - Kim van der Woerd
Billie Joe Rogers
$950.00 17 Register

Schedule clarification: Online courses begin on the first date listed and end six days after the last date listed. The interim dates/times are not your actual online class times.

What will I learn?

By the end of this course, you will be able to:

  • 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 will I learn?

  • Advance readings
  • Individual and group exercises
  • Case studies
  • Mini-lectures
  • Group discussion

Who should take this course?

This course is suitable for professionals who want to deepen their knowledge of transformative evaluation and learning. 

How will coursework be assessed?

Your coursework will be assessed based on your class participation, in-class assignments and a post-class reflection assignment. 

Textbooks and learning materials

We will provide custom course materials.

Technical information

You’ll access online courses through Canvas, our online learning management system, which is available through the SFU website. Canvas is extremely user-friendly—even if you’ve never studied online before. All you need is a computer with an internet connection. 

You may log into Canvas beginning on the first day of each course. For a list of Canvas-supported operating systems and web browsers, visit our supported web browsers page.

To learn more about Canvas, visit the SFU Canvas Student Guide.