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

Abstract

There has been growing attention in HCI to the potential for community-based participatory research (CBPR) to cause harm to community partners. Extractive research is when researchers take ''data'' (i.e., stories, knowledge) and other resources (e.g., time, labor) from communities but provide little in return. Scholars have examined 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. We reflected on our process to co-analyze workshop data from a community-led initiative through member-checking interviews and a duo ethnography. In this paper, we detail the co-analysis approach we used and examine how structural power can incentivize extractive research practices. We pose that co-analyzing data according to community-defined questions can mitigate harm and advance community partners' goals.