Processing qualitative data

Now that all these interviews are recorded they need to be transcribed and coded so that I may use them in my analysis of the Montreal waste management and UA systems. Initially I was going to get a work-study student (who would need to be completely bilingual as I have interviews in english and in french) to transcribe all my interviews because I needed to continue to make progress on writing all my chapters in order to graduate on time. Still, this transcribing was time-sensitive, and the work-study office wasn’t getting back to me. In the end, I bit the bullet and did the transcriptions (and got a little help from a friend).

Although my hands and for-arms were exhausted from the constant typing, the transcribing wasn’t as bad as I thought it would be. It took me about an hour and a half per 15 minute interview, pausing and rewinder on itunes (from my voice memos on the iphone, as the other digital recorder only worked with a PC). I transcribed each one in a separate word file, indicating when I was talking, and using a line or two of space between each “sentence” for the responses in order to do an initial separation of ideas and make it easier to read ( another friend of mine showed me their transcription files so I was able to mirror the style).

The next step was coding. I talked to my social science friends and they recommended MAXQDA and Dedoose. It seems like people learn MAXQDA in class and is pretty standard (and they have a Mac version now which means I can use it, and they do have a student license price). Dedoose seems to have the same functionality but its an online platform and you pay a monthly subscription.

In the end I went with Dedoose because they had good online tutorial videos, and because I liked the idea of learning how to use something that others could also use in future collaborative work (as it isn’t expensive and online). Coding went pretty well, although the site would often “crash” and ask me to log out and in again. Luckily I never lost any of the coding though, so it didn’t seem like too big of a deal.

I ended up with 27 themes (I didn’t predetermine my themes, I let them emerge from reading all the interviews once, and then coding). I think some of them can probably be combined into larger themes though. I plan on recoding everything and comparing the 1st and 2nd try, but using broader themes on the second round. One code that Dedoose suggested in a tutorial was also quite helpful: the “Great Quotes”. I was able to download the file with the “Great Quotes” (and associated codes for the excerpts), translate them and also note where I think they could fit into the structure of my article. I also downloaded code counts, concurrence, and a word cloud of the code application to help me in my analysis.

I can definitely say, after my 1st analysis of the interview materials, that this line of inquiry has really added a lot of richness to my understanding of the Montreal system and the facilitators and barriers to P recycling through composting. The interviews not only added confirmation to some of the aspects I had previously identified, they gave me additional information about two potential barriers I had not put at the forefront of my analysis previously. They also made me totally rethink about alternative solutions to present in the discussion (because of these barriers).

more views while traveling to interview key participants at their place of buisness

In mainstream news, I was so happy to see National Geographic talking about urine recycling in the US (thus P recycling!!!!).


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