Community-Driven Data Analysis: Advancing Methods to Achieve Community Goals in Collaborative Research

There has been growing attention in HCI to the potential for community-based participatory research (CBPR) to cause harm when researchers extract ``data'' (i.e., stories, knowledge gained from lived experience) from communities while providing little in return---levying an epistemic burden on collaborators. Scholars have begun to examine collaboration practices, but this work has yet to focus on data analysis. We (academic and community researchers) explore the benefits, challenges, and power dynamics involved in collaborative analysis. Using member-checking interviews and a duo ethnography, we reflected on our experiences of co-analyzing workshop data we collected in a community-led initiative. In this paper, we detail the co-analysis process we used and examine how structural power can incentivize extractive research practices. We pose that collaboratively analyzing data according to community-defined questions and goals can mitigate epistemic burden. We offer recommendations for developing co-analysis practices that generate an epistemic benefit for community partners.