Collaborative Data Analysis

Why the Widespread Lack of Inclusion?

Data analysis is commonly understood to be a highly skilled activity that requires in-depth training to do well. It is a time-consuming endeavour that is widely perceived to be tedious, difficult and somewhat arcane. It can be very technical and, when conventionally [Page 122]approached, demands a high level of literacy, numeracy or both. Consequently, community members often opt out of this stage. Some are never invited. It can be argued that diverting community expertise, time and attention towards acquiring and polishing analytic skills may be an inefficient and inappropriate use of limited resources (particularly if academic partners are well positioned to take on these tasks).

In terms of promoting more equitable research relationships, it is not important for everyone to necessarily take on an equal share of all the work. Teams may decide that certain members are better suited to take on some tasks, while other team members pick up the slack in different areas. What is important is that everyone be given the opportunity to participate in those activities that they are interested in and able to perform. Furthermore, promoting equity may mean providing opportunities to build the skills and capacities of team members to engage in work that they are excited about and to find ways to be more inclusive.

Lack of community involvement in data analysis and interpretation may exclude those with the most to lose from important choices about shaping and interpreting study findings. When certain groups are systematically excluded from data analysis, we need not only to ask why but also to challenge ourselves to imagine how these barriers can be overcome. Rather than adopt a deficit model (i.e. considering community members to be unskilled/immature/illiterate/impaired), many researchers are finding ways to build on the skills, talents, competencies and wealth of knowledge of community members to engage them in accessible analysis opportunities. Recognizing that community members may see and understand the world very differently from researchers, these pioneers of collaborative analysis are creatively finding new ways to make the work inclusive and (often) more fun.

Old Methods, New Possibilities

Recently, several studies have begun documenting their participatory processes. Researchers partnering with children or youth, adults with intellectual disabilities and other marginalized populations have been at the forefront of the movement to advocate for and create more inclusive research practices.

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