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Research assistants shape SFU: What’s Next? analysis

September 20, 2022
Recruited from a range of backgrounds and disciplines, seven research assistants analyzed and collated every thought shared by SFU community members to identify the themes and priorities that will help guide the university in the years to come.

Earlier this spring, SFU President Joy Johnson asked: "SFU, what's next?" And the community responded, with more than 700 thoughts shared on the online platform ThoughtExchange in response to questions like "What is SFU's next bold move?" and "What differences should SFU make in the world around us?"

But how do you draw conclusions from such a large and varied dataset in a way that minimizes bias and ensures that perspectives are fairly represented?

In this case, the answer lay in the hands of seven SFU research assistants, hired to take on this task.

Recruited from a range of backgrounds and disciplines, Maria Abarca, Aylar Adeh, Thuy Do, Stan Hetalo, Catherine Jeffery, Elina Jin and Marie Pitre analyzed and collated every thought shared by SFU community members to identify the themes and priorities that will help guide the university in the years to come.

“Having researchers with a variety of academic and personal backgrounds is critical for any project, especially one that is happening on such a large scale and with such big potential impacts,” says Catherine Jeffery, a research assistant who recently finished her master’s degree through SFU’s School of Communication.

“I think most researchers will admit that no matter how objective things seem, our opinions and perspectives will always be shaped, to some extent, by our own lived experiences and the formal training we’ve received. In my view, having a diverse team helps ensure that our findings – and the recommendations that follow them – are well rounded.”

Working under the supervision of Laya Behbahani, director of the Student Experience Initiative and project lead for ThoughtExchange analysis, each research assistant independently read, interpreted, and coded the participants’ responses into themes that were influenced by their unique personal lenses. Those lenses included the research assistants’ different disciplinary traditions, such as economics; education; global humanities; mathematics; public health; gender, sexuality and women’s studies; and communication.

'When you inductively code, you’re really allowing the answers to emerge on their own, as opposed to prescriptively applying a set of priorities. The data collection was grounded in letting the community guide what they wanted to set as their priorities.'

- Laya Behbahani, director of the Student Experience Initiative and project lead for ThoughtExchange analysis

They were not provided formal training – other than basic onboarding – to allow an organic interpretation of the data, and to discourage preconceived themes. The methodology, Behbahani explains, was based on grounded theory and approached through inductive reasoning:

“It was important that we didn’t have a preconceived codebook,” says Behbahani. “When you inductively code, you’re really allowing the answers to emerge on their own, as opposed to prescriptively applying a set of priorities. The data collection was grounded in letting the community guide what they wanted to set as their priorities.”

The research assistants then reviewed and refined themes to ensure that all thoughts were captured and assigned to a theme. Next, they returned to the raw data and used a constant-comparative method to ensure themes were grounded in the data and consistently applied between questions.

Finally, the results were named, shared between research assistants and ranked from most to least importance, based on the ranking assigned by ThoughtExchange participants.

“When I joined the team meeting for the first time, I recognized that people were from different disciplines, different cultural backgrounds, even different language backgrounds,” says Elina Jin, an international student who recently completed her master’s degree from the Department of Global Humanities.

“I noticed people were using different terms to describe similar data. I think that because of our background differences, our knowledge didn’t necessarily share the same wording, but the results were very similar. It was amazing to see because we are all from different backgrounds, culturally, linguistically and academically.”

An international student from China, Jin was thrilled to be part of the process and included in the work.

“Speaking as an international student, I find that normally our voices are underrepresented. Our English is not great and – especially for people from cultures that are more collective in nature – we tend not to share our feelings. It made me really believe in SFU’s desire for engagement.

"The school was open to our voices- both by using the ThoughtExchange Platform and getting diversified RAs involved to analyze the data. And I really appreciate it.”

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