ABSTRACT
Establishing and proving methodological rigor has long been a challenge for qualitative researchers where quantitative methods prevail, but much published literature on qualitative analysis assumes a relatively small number of researchers working in relative proximity. This is particularly true for research conducted with a grounded theory approach. Different versions of grounded theory are commonly used, but this methodology was originally developed for a single researcher collecting and analyzing data in isolation. Although grounded theory has evolved since its development, little has been done to reconcile this approach with the changing nature and composition of international research teams. Advances in technology and an increased emphasis on transnational collaboration have facilitated a shift wherein qualitative datasets have been getting larger and the teams collecting and analyzing them more diverse and diffuse. New processes and systems are therefore required to respond to these conditions. Data for this article are drawn from the experiences of the Innovations for Choice and Autonomy (ICAN) Research Consortium. ICAN aims to understand how self-injectable contraceptives can be implemented in ways that best meet women's needs in Kenya, Uganda, Malawi, and Nigeria. We found that taking a structured approach to analysis was important for maintaining consistency and making the process more manageable across countries. However, it was equally important to allow for flexibility within this structured approach so that teams could adapt more easily to local conditions, making data collection and accompanying analysis more feasible. Meaningfully including all interested researchers in the analysis process and providing support for learning also increased rigor. However, competing priorities in a complex study made it difficult to adhere to planned timelines. We conclude with recommendations for both funders and study teams to design and conduct global health studies that ensure more equitable contributions to analysis while remaining logistically feasible and methodologically sound.
