I am in no doubt that after the previous reading session it is difficult to pigeon hole my approach. Even the development of the coding scheme, I am finding that my approach consists of ideas from, though not limited to, inductive reasoning, thematic analysis and grounded theory. There is another idea that I have been working on for quite a while but now am in a position where I can make significant progress in this idea. Still plenty to do, but I am thinking that this research will become a multimodal project. It’s really a case of thinking about the way that is best to present this idea in the thesis, and to think more about the way in which the coding scheme relates to this idea. Now that the coding scheme is improving and I feel is beginning to take the form I want it to take, it is expected that there shall be some significant movement in developing this other idea in the coming weeks.
Along with that, another key current task is to continue to rework the coding scheme: reread the data segments, recheck the coding, and drop codes or amend them as necessary as well as combining data segments to present a complete meaning if I feel that I have divided them a bit too much. The idea also is to continue to go through the rest of the data and recode the data as necessary to reflect new meanings and new insights I am making whilst editing on paper, which is also a continuous process.
The process of rechecking everything, as just mentioned, is being carried out on paper. I have used computer software various times to amend the coding and to think about the data in various ways but sometimes for some objectives, it is best to simply use pen and paper. Print out all of your coding, relevant theoretical memos and other relevant documents and go through everything by hand. This approach I feel is especially relevant to mine because I am comparing a lot of data, within and across data sets, in order to develop the categories and themes, and also to develop an understanding of the behaviour of the phenomenon. This is not to suggest that there is no value in computer based analysis and I plan on using that further in the future.
I am pleased that I made the choice to do this, because I have found new insights and ideas that were not at all obvious when staring at the computer screen. Being at the computer screen and being concerned with navigating the software in order to find relevant pieces of data sometimes distracts you from your objectives and can cause you miss out on important insights. Simply doing things by hand sometimes can really help you find new insights that perhaps were not so obvious before. That being said however when I was editing everything on the computer following the edits by hand, there were some insights I made on paper that actually did not make sense when I really thought about what the particular pieces of data actually meant. It’s like a constant battle between colliding thoughts inside your mind with regards to the meaning of the data and what any particular data represents, but my experiences tell me that sometimes using pen and paper is best.
When you have these colliding thoughts and when you are able to perceive and interpret any date segment or segments in various different ways, remember to think about the context within which the segment is situated and remember that whatever you observe and interpret must be relevant to your research questions.
Regardless of what methods are used to explore the data, as always the key idea is to keep asking questions about the data. Remember that the data is a representation of the phenomenon of interest, and what you observe and place meaning upon might not be the same between multiple researchers. Here, you have to try to make sure that what you observe and what you interpret is as close to the data and as close to some sense of objectivity as is possible, if that is a desire of your research. Therefore, I am continuously asking questions about the data and also about my own observations and interpretations, and the quality of those observations and interpretations.
The only way you can progress is to ask questions. Making an observation or an interpretation of something within the data is fine, but you have to be sure of what it is you are really observing or interpreting. This is a process that I am only just scratching the surface of describing the process here (seriously, it’s taking up pages of my research design chapter!), but it is a process that is worth investment and engagement with. You need to make time and effort in ensuring that your observations and interpretations are as sound as possible, regardless of their ultimate subjective nature.
Just keep asking questions about the data and your own interpretations. You learn and develop only through asking, rechecking, reconfirming, and asking again! And when you are sure you are done with everything and have all the answers (quite frankly, I doubt claims of this sort), then ask more questions again!
Keep asking questions and keep going!
‘till next time!