The Container Store and Staples are two of my favorite stores. I love to organize, categorize, and sort almost anything – clothes, Tupperware, cleaning supplies. Everything has its place (or at least I preemptively try to see to it that everything has its place since I have a partner who thinks otherwise). This notion of everything having its ‘place’ is definitely something that spills over into my studies and research process. I have file cabinets and shelves of binders – filled with readings, research data, notes, outlines and more. (It is a hoarding that goes back to high school Biology. I still have hand-drawn sketches of the heart and lungs on yellow lined-legal paper.)
So, when I discovered qualitative research and, more specifically, what I thought were some basics of data analysis, I felt like I found my ‘place’ yet again. Instead of keeping meticulous Biology notes or color-coding clothes, I collected binders of data (interview transcripts, images, artifacts, field notes, memos, etc.) that could be organized, labeled and categorized. Part of this organizing included a process I began to know as coding. Or, at least what I thought coding was…
I started to learn about coding from ‘traditional’ qualitative textbooks (e.g., Bogdan & Biklen 2007; Creswell 2013; Emerson, Fretz, & Shaw 2011; Horvat 2013). There would typically be one chapter in these introductory texts on data analysis, with a subsection of that chapter on coding. It was certainly helpful. The writing and definitions in these books provided a guide. They often positioned data analysis as a linear process, with coding perhaps the beginning of it.
With this advice, then, I opened my binders of transcripts, images, and notes and started to go through each piece – to ‘look’ for codes. I relied on Creswell’s (2013) explanation in which “coding involves aggregating the text or visual data into small categories of information…” (p. 184) and suggestions from Bogdan and Biklen (2007) to search “for regularities and patterns” (p. 173). I followed a recipe-like approach, reading through data for words and phrases to be sorted into larger categories and then themes. With my stash of colored pencils and a composition notebook, I circled and made notations of key words and common ideas. I created a detailed excel spreadsheet of when each of these ‘codes’ were mentioned.
Of course, I was still thinking about my research questions, theoretical framework, and related literature. Yet, as I kept coding in this way, the data seemed to move farther away from me and the experience of my actual research process.
Let’s go back… or sideways.
My dissertation research took place over 18-months. I spent 2-3 days a week at an after-school program. I wrote fieldnotes, did interviews, collected artifacts, and used arts-based methods such as visual journals and scrapbooking. I catalogued everything in my precious binders. I created a data organization guidebook, which is basically a table of contents, breaking down the process and what I had gathered. I accumulated more binders and made more trips to Staples. A friend refers to me as a classier version of those hoarders you see on reality television shows.
I had so much information but, as I followed this traditional way of coding, it was no longer making sense to me. It felt impersonal…passive. I had spent so much time – doing research, working with youth, and in this after-school context. I had been immersed in the research, from planning to facilitating to tweaking that something felt amiss with the linearity of this coding. The mechanics of coding was pulling me away from my embodied research process (Ellingson, 2017). It also did not match the theoretical framework I sort of settled on. (See blog on theory for more.)
I struggled.
I thought I had to take all the data I had amassed – code, categorize and sort it into themes. That’s what your dissertation is – right? Aren’t you supposed to present everything that you have discovered in a neatly, condensed summarized version? And doesn’t it begin with coding? Open coding, inductive and deductive coding… until categories and themes ‘emerge.’
My short answer is NO.
And, fortunately, with some help from Maggie MacLure (2013), I discovered a different kind of coding.
Building upon post-structural and post-humanist research, MacLure (2013) sees “coding as an analytic practice” (p. 174). With this approach, coding is not about searching for recurrences and patterns or reducing data into explicable categories. Instead, coding becomes an open-ended, ongoing process in which we (the researcher) immerse ourselves with “the minutiae of the data” (p. 174). This means that, when we engage with our data, it is okay to linger in those unexpected, sticky intensities that captivate us, but we can’t quite explain. This means that we can “welcome and pause” (p. 173) in moments that fascinate, confuse, and invigorate us as we become more deeply absorbed within our data.
This advice helped. It provided the impetus to shift from more hierarchical and passive coding. It led to my paper in Sport, Education and Society (Safron, 2020) that I continue to refer to in this blog.
Let me explain (and I apologize if this is somewhat repetitive).
Over the course of my 18-month dissertation research, which included a nine-week scrapbooking project, I collected and organized data into numerous 3-inch binders. I first worked through the data, coding and categorizing in linear ways. Yet, there was something about this process that did not match the struggles, concerns, and questions I had during my research (and continue to hold onto). There was also this one week, week seven of the nine-week scrapbooking project, that kept pushing at me, making me curious to explore further. As I have previously written, week seven of the scrapbooking project was a pedagogical encounter (Tinning, 2010) between myself, four young participants, and two fitness professionals. Our one hour and 45-minute encounter that day initially generated a 40-page single-spaced transcript. It also led to further conversations with the youth, a follow-up interview with the fitness professionals, and more questions from/for myself.
Still, this was ONE week out of 18-months of gathering my dissertation data. Was it OKAY to take a small snippet of data and really immerse yourself with it? What about everything else??
MacLure (2013) and others (Allen, 2015; Jackson & Mazzei, 2012) gave me the confidence to say YES – it was OKAY. So, I allowed myself to pause with the data generated from week seven of the scrapbooking project. As I wrote in my paper, I read and re-read this data, highlighting moments or fragments that ‘glowed’ (MacLure, 2013). In all honesty, I never felt (or feel) I can properly (re-)present that pedagogical encounter through my writing. But, this (new) coding practice created a closeness and intimacy that I would not have discovered if I had glossed over the data from this encounter. If I had not let moments from this encounter gain intensity, such moments would have been buried amongst the rest of the data I had amassed throughout my research.
So, what does this mean? Is this still coding? Or was I no longer coding because I wasn’t sorting things into neatly defined categories and themes? I am not sure I can answer that question.
There continues to be debate around the concept of coding and I am certainly no expert on it. Some scholars (St. Pierre & Jackson, 2014; St. Pierre, 2013) argue that coding should not be used with post-theoretical (structural, humanist, feminist) perspectives. They say traditional coding reproduces what we already know, strips words from their context, and creates binaries. MacLure (2013) argues that, while traditional coding fails (or even offends), coding should not be abandoned altogether. It can be an open-ended experimentation that does not settle on fixed entities. Bhattacharya (2015) explained how ‘coding is not a dirty word’ – but coding should be driven by theoretical perspectives, as well as a researcher’s ontology (being) and epistemology (knowledge). She presents a way of coding with NVivo that moves between “a need to organize and a tendency toward non-linear thinking” (p. 13). She demonstrates how this digital process leaves things open – and allows her to have a clean house.
I am not so lucky. In my coding, I gravitated towards MacLure’s (2013) manual process “with paper and pen, scribbling a dense texture of notes in margins and spilling onto separate pages” (p. 174). I re-read transcripts, keeping my colored pencils and scissors close. I made notations and added sticky notes with questions or ideas. I brought in images, keeping them whole or cutting them up. I highlighted readings, pulling out phrases and concepts that connected to the data that I was engaged with, sometimes writing phrases in margins and other times creating a colorful paper as a reference point.
Admittedly, in this coding, my approach has never been the same twice. It is structured and creative, orderly and disorderly, active and passive. My practice expands or alters depending on details that gain intensity, becoming preoccupations and leading to possible openings.
With my Sport, Education and Society paper, MacLure’s (2013) notion of coding enabled me to start with data from the seventh week. I focused on the transcripts and related data, becoming familiar with the encounter again and again, while also trying to leave myself open to things I could not easily explain – and be ‘guided’ by affect theory. In my dissertation more broadly, MacLure’s (2013) approach became a focal point. I began to explore moments that ‘glowed’ (or gained intensity). It produced a different engagement with my data, shifting the writing within my dissertation. I still have not come up with themes for all my data. I still have not written on much of what I discovered. It is possible I never will. I am OKAY with that… I think.
So, as I reflect on what I did (and possibly think about the future), maybe the question is not whether to code or not to code – maybe the question is how am I coding and what is driving my coding (if I choose to do so)? Again, I have no answers but, I do want to leave you with some suggestions that I have found helpful and perhaps you will too.
1. The structure of creating a data organization guidebook helped. Even if I do not use traditional coding (or coding at all), I emphasize the importance of starting with some system that works for you. I rely on my binders and organizational guidebook because they continue to provide a reference point of everything I did during my dissertation. The manual process of creating it also helped me to ‘get to know’ my data better. (And I get to return to Staples and The Container Store.)
2. Do not hold onto that data organization guidebook for dear life. Well, make sure it doesn’t burn and that you have everything backed up but, let go of structures that bind you to certain perspectives. Do not get stuck in traditional linear trajectories of coding. Coding can be an embodied process that brings us closer to our data but, we have to be willing to take risks. I had to let go of a fear of being unable to neatly explain things and try to embrace the uncomfortable and inexplicable. Doing so can allow us to see and engage with data in different ways, perhaps sharing a new perspective.
I remain drawn to part of the opening of MacLure’s (2013) chapter on coding:
I argue that there is a languorous pleasure and something resolute in the slow intensity of coding – an ethical refusal to take the easy exit to quick judgement, free-floating empathy, or illusions of data speaking for itself. More importantly, when practised unfaithfully, without rigid purpose or fixed terminus, the slow work of coding allows something other, singular, quick and ineffable to irrupt into the space of analysis. Call it wonder (p. 164)
With this, I urge you to do what is right for you, your data, and your research. It may be that traditional coding suits your needs, but I also wonder what you may find when you pause and open up to the unpredictable – and then what additional stories we may share and build upon.
References (for further engagement):
Allen, L. (2015). The power of things! A ‘new’ ontology of sexuality at school. Sexualities, 18(8), 941–958. https://doi.org/10.1177/1363460714550920
Bhattacharya, K. (2015). Coding is not a dirty word: Theory-driven data analysis using NVivo. In S. Hai-Jew (Ed.), Enhancing Qualitative and Mixed Methods Research with Technology (pp. 1–30). Hershey, PA: IGI Global Publishing.
Bogdan, R., & Biklen, S. (2007). Qualitative research for education: An introduction to theories. Qualitative research for education: An introduction to theories (5th ed.). Boston: Allyn and Bacon.
Creswell, J. W. (2013). Qualitative inquiry & research design: Choosing among five approaches (3rd ed.). Thousand Oaks, CA: Sage.
Ellingson, L. L. (2017). Embodiment in Qualitative Research. New York, NY: Routledge.
Emerson, R. ., Fretz, R. ., & Shaw, L. . (2011). Writing ethnographic fieldnotes (2nd ed.). Chicago: The University of Chicago Press.
Horvat, E. (2013). Making sense of what you are seeing: Analysis and writing. In E. Horvat (Ed.), The beginner’s guide to doing qualitative research: How to get into the field, collect data, and write up your project (pp. 105–124). New York, NY: Teachers College Press.
Jackson, A. Y., & Mazzei, L. A. (2012). Thinking with theory in qualitative research: Viewing data across multiple perspectives. London: Routledge.
MacLure, M. (2013). Classification or Wonder? Coding as an Analytic Practice in Qualitative Research. In R. Coleman & J. Ringrose (Eds.), Deleuze and Research Methodologies (pp. 164–183). Edinburgh, UK: Edinburgh University Press.
Pierre, E. S., & Jackson, A. Y. (2014). Qualitative data analysis after coding. Qualitative Inquiry, 20(6), 715–719. https://doi.org/10.1177/1077800414532435
Safron, C. (2020). Health, fitness, and affects in an urban after-school program. Sport, Education and Society, 25(5). https://doi.org/10.1080/13573322.2019.1625318
St. Pierre, E. A. (2013). The appearance of data. Cultural Studies - Critical Methodologies, 13(4), 223–227. https://doi.org/10.1177/1532708613487862
Tinning, R. (2010). Pedagogy and human movement: Theory, practice, research. London: Routledge.