- 1. What is data analysis?
- 2. A grounded theory approach to data analysis
- 3. Processing your data in preparation for analysis
- 4. Coding interview transcripts and other data
- 5. Analysis in a collaborative context
- 6. Critically evaluating your data: reflexive data analysis
- 7. Summary
- 8. Feedback Survey
5. Analysis in a collaborative context
Research projects that are interdisciplinary, comparative, or participatory in nature may involve collaborative data analysis.
Collaborative analysis can be very productive in terms of allowing for a division of labour between different people and broadening the range of perspectives on the data to produce a more rich and complex analysis.
However, undertaking data analysis in a collaborative context also has challenges. Collaborative analysis requires ensuring that the frameworks for processing and analyzing data are:
- Developed early in the project,
- Are comprehensive,
- Are consistent,
- Are communicated clearly across the research team, and
- Able to be revised in consultation with the team.
If collaborative data analysis is occurring in the context of a participatory research project, where members of the community being researched are involved in the analysis process, strategies need to be developed to ensure that the data analysis process is conducted in a consultative way that allows different voices to be heard. For example, rather than circulating a codebook among researchers, a series of workshops may be held with community members to work through the data in a more inductive way and brainstorm a range of themes that could act as codes, alongside a range of new questions to emerge from the data.
Similar issues also arise in the context of comparative research projects, where data analysis may have to be done at several stages and at several scales. The organization of the data analysis phase can be crucial in determining how and when data gathered at different sites will be made comparable.
If you are working in a collaborative team, consider:
- what definitions of central concepts you will use, in cases where there are contested meanings between different people involved in the analysis.
- How will the data analysis process be divided among the different people involved in the team? What is the best way to work with the skills, knowledge and expertise that different people bring to the project?
- Will you schedule regular team meetings during the analysis process, either in person or virtually, to evaluate how smoothly the process is moving, and to work through any challenges that present themselves?
- At what point will data from different sites be compared?
Crow, G.M., Levine, L. and Nager, N., 1992. Are three heads better than one? Reflections on doing collaborative interdisciplinary research. American Educational Research Journal, 29(4), pp.737-753.
Moss, P., 2002. Taking on, thinking about, and doing feminist research in geography. Feminist geography in practice: Research and methods, pp.1-17.
Sharp, J., 2005. Geography and gender: feminist methodologies in collaboration and in the field. Progress in Human Geography, 29(3), pp.304-309.
Woods, P., Boyle, M., Jeffrey, B., and Troman, G., 2000 A research team in ethnography, International Journal of Qualitative Studies in Education, 13(1), pp.85-98.