For most of my study planning, I have been planning to use the theoretical propositions in my analysis of my data, and I believe that I still will. What I’d love to press more into is the extent to which that must necessarily be diametrically opposed to a ground up/grounded theory approach. The whole point of my action research is, in some sense, to gauge the extent to which the students find value in the programming I bring. But I would also like to explore their insights and offerings on their own terms. I have not explicitly committed to an approach yet, but I believe that I will be testing my theoretical propositions, and I think that makes the most sense. I am fairly sure that if I go that route, I will need to be very conscious in my coding about the extent to which I am actively looking for data that confirms my propositions.
I will likely attempt to use pattern matching in my case study data analysis when reviewing the documents and artifacts that come out of the students’ CLD and design challenges. I will be interested to explore whether or not patterns emerge in how students approached those challenges, particularly with respect to the previous instruction and content I provide in my innovation. For example, as students approach their designs, I may find particular patterns with respect to how students orient to the challenges, or perhaps even to how they articulate the challenges in the first place. Likewise with solutions, I may find that students have similar procedural or process-driven approaches.
However, I will admit that as a novice analyst in this technique, I find myself a bit intimidated about my efficacy to pattern match. However, I am reasonably confident that I can develop this skill prior to the fall data collection.
I will almost assuredly use some form of explanation building in my case study analysis. Given that I will be relying on several different sources of data, I will have rich opportunity to dig into the artifacts, interviews, and observations to create explanations for what is happening in the case, how it is happening, and make some explanations as to why. As Yin (2018) notes, “the gradual building of an explanation is similar to the process of refining a set of ideas” (p. 181). In this way, I believe that explanation building is an important part of design synthesis, and so it will be a natural fit in my analysis of the data for my action research. I will have to account for rival explanations and hypotheses, and my supervisor and mentors have given me some good advice on this front: they have suggested that when I look at my data and build my explanation, that I need to use contra-positive thinking techniques to disprove or alternative assert the explanation I want to build. I will definitely be trying that out.