Data analysis

In qualitative research, the distinction between data collection and analysis (and writing) is somewhat fluid. Rather than being separate processes, data collection and data analysis often take place simultaneously and are closely interlinked. According to Maxwell (2013, p.

104), it is a big mistake to let field notes and collected material pile up unanalyzed, as this makes analysis and writing much harder. Therefore, qualitative data analysis is often highly iterative, as was the case during my research. This means that data collection and analysis go hand in hand and repeatedly refer back to each other (Bryman, 2004, p. 401). According to Maxwell (2013, p. 2), a qualitative research design is usually more flexible rather than fixed, and should aim to be reflexive through all stages of a project. Maxwell (2013) proposes that qualitative research should follow an interactive model, where the research questions are at the center, and where goals, conceptual framework, methods, and validity are

“satellites” outside, comprising one’s research design. The researcher will constantly move back and forth between the center and the satellites.

I my case, the above-described process was especially evident, since I traveled back and forth to the “field” (or rather several “fields”). While many development researchers carry out long-term ethnographic fieldwork, I made shorter, intensive field visits to Tanzania, and collected data and held other interviews also in between these trips. The inability to stay in

74 the field for long periods of time can have a downside, such as having to reconnect with one’s network of contacts or not being as flexible about the timing of interviews. However, I found it useful to leave the field with my head and notebook full, as this allowed me to analyze along the way. Having some distance was useful in this sense, as I could “wind up” preliminary findings and look more into which directions I would like my research to go. Moreover, by spreading my visits to Tanzania over two years (April 2015 to March 2017), I was able to follow the development of SAGCOT over time. A lot changed during those two years, and I would not have been able to see the changes as clearly if, for example, had I stayed in Tanzania for six full months during the first year of my data collection. Furthermore, it was useful to work with my research questions simultaneously, as this allowed me to have a better perspective, and helped me understand connections along the way until the very end.

It was, for example, a deliberate choice to write Paper 3 last, as categorizing discourses and analyzing them was ongoing in the back of my head from the very start of data collection.

During fieldwork, both in Tanzania and at all events, as well as when conducting interviews in Norway, I kept rich field notes. I took notes while interviewing, almost without exception.

For the less structured interviews, observations and other methods of data collection, I found time afterwards to write a summary of the information I had collected, as well as some brief thoughts about it.

In addition to field notes and interview transcriptions, I kept memo books for the development of the research project. Memos are often used by researchers as a tool to help them to “crystallize ideas and not to lose track of their thinking on various topics” (Bryman, 2004, p. 405). In exactly the same way, memos were valuable during my research.

Furthermore, memos are useful as analytical, conceptual and theoretical notes, and through memo writing, “researchers gain analytic distance from data and create an intellectual workspace for documenting their analysis” (Charmaz, Thornberg, & Keane, 2018, p. 429).

Memos helped me structure overall thoughts and notes throughout the data collection and analysis process. I kept information in my memo books about which decisions I made along the way and why I made them. It was a process of “thinking out loud” along the way. I kept my current memo book with me wherever I went. In the memo books, I created matrices of findings, drew Venn diagrams, sketches, maps, and timelines, recorded “brainstormings,”

75 and employed other visual techniques that proved very helpful. I color-coded theories with findings, and used tables to identify research gaps. The continuous work functioned as a key data analysis tool for me. A few examples of such memos are attached in Annex 3.

As outlined above in Section 4.2.2, I recorded most interviews. When I was doing fieldwork and recording a lot of interviews during the daytime, I aimed at transcribing at least a few interviews every evening. However, transcribing is extremely time-consuming, so I had to spend several months on the task retrospectively. I transcribed all recordings myself and used only standard computer software (VLC media player and Microsoft Word). For the purpose of discourse analysis (see Section 4.2.6), I usually transcribed and took notes of everything in the original recording, including details such as silence, hesitance or tone used when talking about something specific.

Additionally, I recorded most sessions I attended during events and conferences. However, in roundtable discussions, several people talked interchangeably and were only introduced in the beginning of the session, so it was difficult to know who the speaker was when listening to the recording. In some sessions, there were also quite lively discussions with questions and contributions from the audience. This was difficult to transcribe. Although the sound quality of the recordings was surprisingly good, given that the recorder was usually placed on my lap among the audience, it was difficult to identify who the different speakers were. Transcribing the recordings of those sessions was time-consuming. Usually, to ease the work, I took notes simultaneously as an assisting tool, or I looked at conference programs to remember who the speakers were. For most of the event recordings, I did not transcribe word-by-word; rather, I focused on statements and quotes, and most importantly the key messages and overall topic of the event. Most sessions at events were not recorded with informed consent, since it would have been impossible to obtain it in advance from all participants, including the members of the audience, who usually participated (Homan, 1991). However, almost all of the sessions were open to the public, and accessible online, either as written summaries or via live streaming. Many of the sessions were also recorded or videotaped for internal use. I did not record during closed sessions. In smaller sessions (with 10–20 participants) I usually did not record, as those sessions and discussions were more “personal” to some extent. On one occasion, when the prime minister of South Korea

76 visited the conference, I chose to turn off the recorder due to the severe safety constraints imposed following his visit.

Finally, I recorded focus group interviews. As noted earlier, the interviews were carried out with the assistance of an interpreter. The recordings were hence long, with much talk in Swahili, and with shorter translations. Nonetheless, recording the discussions was useful because I was able to sense the heat and direction of the debate, and note who talked the most and which topics that engaged the informants the most. I do not speak very well Swahili, but I could understand bits and pieces and key words related to my topic and hence get a sense of the discussion. Furthermore, while the interpreter’s translation revealed an answer to my question, I could to some extent sense the level of agreement within the group by listening to the recording.

When analyzing interviews, memos and field notes, I used the technique of color tagging and cross-referencing between interviews in printed hard copies of transcribed interviews or other documents. Coding of data means reviewing data and breaking it down into categories, boxes or labels. Coding qualitative data is usually a process in constant change, given that usually such data analysis is a continuous process. For formal documents, it was useful to be able to count words, for example how many times the word “sustainable” appeared in a text, including how the use of the word and the frequency of it, has developed over time.

Document analysis implies analyzing the content of texts, but also an overall hermeneutic analysis (Bryman 2004). It is therefore closely tied with discourse analysis, which also emphasizes linguistic text and content analysis (Peräkylä & Ruusuvuori, 2018).