by Sarah Strada
This week in our research lab we worked a lot of analyzing the data we have been coding. I am working on the Agency Project which is looking at the relationship between the government and the community on the issues of malaria and wildlife conflict. I quickly realized that analysis is a lot messier than coding! I started by pulling out major themes and separating those on an excel spreadsheet. Then I broke those themes down into more specific themes. I felt like I could have gotten even more specific but then I realized that 3 hours had pasted and I had 3 more codes to analysis. It is really easy to get lost in the analysis but I felt like I noticed things about the data I hadn’t seen before and I felt like I was really beginning to understand it. It was a really rewarding feeling.
After the agency team had gone through all their data once we met to discuss some of the themes we saw. As far as wildlife conflict goes, the overall theme was: fence. The government built a fence to deal with wildlife conflicts, the community felt this fence was of really low quality and it was pointless, and the community thought the best way to solve the wildlife problem was to build a fence . . . fence, fence, fence, so many things about the fence. It really made me want to organize a service trip to Mozambique to build them one the best fences this world has ever seen.
Anyway, after we met we all went through our codes again and started to combine them all onto a single excel spreadsheet for each issue (one for malaria and one for wildlife conflict). Now we were all separating the codes into the same themes so they can be easily combined later. Going through the codes this time, I left a lot more out because at the end of the day I just had to accept that not every interesting thing said adds to the purpose of our paper. This analysis has been difficult but I often found myself unable to pull myself away from it. It felt like a puzzle that I had to finish solving.
Setting boundaries in research is one of the toughest things to learn to do, and not easily teachable. There are all sorts of fun and interesting ways to look at data, and analysis is supposed to generate more questions. However, if a researcher doesn’t narrow down their topic the analysis and final writing can get unmanageable and frustrating. Sometimes the best way to learn is just to dig in and see. ~JS