Posting this summer has become an elusive task for me. As much as I meant to chronicle my summer of analyzing data, I have not been inspired to do so. Partially, I have been surprised how far behind I feel in my effort. I suppose I am not really, but there is a bit of mental game being played when spending hours reading transcripts, watching videos, and coding it all. I have a long way to go, but what is emerging from the data is both affirming and surprising. That is the fun part. Yes, tiring to be sure, but also fun. It is like I am on a treasure hunt–a long, at times boring, yet exhilarating treasure hunt.
I will get back to data analysis shortly, but first a quick reflection on my time at SREB’s High School’s That Work conference. This is my third year attending and second year presenting. This year surprised me. My first year was a whirlwind, but the sessions I attended were not particularly memorable or helpful. Just bad luck picking sessions, potentially. Last year, all I did was present, so my fault again for not truly attending. This year, I was far more engaged and there were some sessions that inspired ideas for this upcoming school year. I am particularly excited about ideas for our advisement program, which is deeply tied to my school’s career academy efforts. We’re going further into uncharted territory this year. The ‘how’ of what we do this year will matter much more than the ‘what’. Buy-in is crucial, which can only really come from a clear vision we all see on campus. Needless to say, I have some work to do these next few weeks. Wish me luck.
Alright, back to data analysis. Data analysis is challenging, I think, for any novice researcher. The road maps and ‘how-tos’ in the field are vast and at times vague. While I certainly spent time reading and researching best practices for data analysis, the truth remains in a qualitative study, data analysis is anything but clear-cut. There is a consensus that open coding is the foundation to data analysis in a qualitative study, but the waters get murky from there. Even open coding as a concept is amorphous at times–at least it was for me. As alluded to earlier, the way I recently tried to explain it to a friend was my data is a massive map and I am interpreting (reading) that map to find treasure; my data is my ultimate treasure map. Truthfully, I did not get a firm handle on the open coding until I started to really open code my own data. Being a few months into it, I feel a bit more comfortable looking at layers of codes and extrapolating emerging themes. The process has not been easy, though. I have actively avoided coding a few days this summer because it is mentally exhausting and literally takes hours just to go through one transcript sometimes. What keeps me going, however, is when I stumble upon something I could only see in the data upon closer inspection–a treasure hidden in the map. I have never felt more nerdy and satisfied as a researcher than when something thematic starts to emerge from the hours of coding and questioning. It is a bit Frankenstein’s monster-esque when I saw my data ‘come alive.’
That all said, I still have a ways to go. As I am doing a cross-analysis case study, I have only fully coded and reviewed two of my cases (each case is one student), and I have two to go. I have timelines and goals, but I also know I am not quite in the timeline I had hoped, but maybe now that I have a better grasp on what I am doing, my other two cases’ data will come a bit easier.
To top it all off, I am an expectant father! 2017 should be a banner year, assuming I can successfully defend my dissertation and somehow keep both my wife and newborn content and happy. Now, loved–absolutely they’ll know they are loved–but content and happy, well I am an novice researcher not a wizard.