Introduction

I am convinced that the PhD process is a treasure hunt to find a secret research handbook using a set of vague institutional guidelines rather than an explicit map. It seems once the handbook is discovered, successful adventurers are sworn to secrecy: my research supervisors have denied the existence of such a handbook, yet their research advice is often ambiguous in the manner of someone who knows the answer to a riddle but does not want to spoil the adventure. While such cautious guidance was helpful for many stages of my doctoral research, encouraging greater critical reflection on my research focus and data collection methods, I struggled to apply this casual mindset to select and justify a data analysis approach. With the secret handbook nowhere to be found, I decided I would retroactively establish my own analytical formula towards the end of my research process, a method that could be communicated through clear stages and compatible with a variety of research approaches. After all, by the last year of my doctoral research I would surely have a very clear understanding of how to conduct data analysis and talk about it. Surely.

This chapter is structured to provide a reflection of my experiences in designing and developing this analytical framework, using examples from a short but intensive period of my fieldwork to provide context to my decision- making processes as I established my research plan and subsequently watched it expand from a neatly structured project to an immersive experience. After providing some foundational information about the overall project and my original plan for conducting fieldwork and data analysis, I detail the different ways my research methods and focus deviated from this plan, the ethical and practical considerations I encountered, and the evolution of how I understand data analysis to relate to research practice. While I offer a framing of my approach as a formulaic analytical process that merges creative practice with thematic analysis that can be used as is or adapted to fit other preferred methods, my experiences in developing this approach demonstrate why a formulaic approach to data analysis is not, and should not be, everything.