Iterativity in postgraduate writing: making peace with the mess

Lovely husband and I were talking recently about a workshop we both attended on postgraduate study, and our respective conversations with our own postgraduate students about what postgraduate study involves them in, specifically the over-and-over again nature of the reading, writing and thinking. Iterativity, we concluded, is the name of the game at this level, and in post-doctoral academic research; yet, it is an aspect of working at this level that produces much frustration, self-doubt and struggle.

Ernest Hemingway famously said: ‘The first draft of anything is shit’. He was, certainly in my experience, right. If you look up writing advice on Pinterest, you will find many soundbites to inspire you; for example: ‘First drafts  don’t have to be perfect. They just have to be written’, ‘A crappy first draft is worth more than a non-existent one’, and writing first drafts as being like ‘shoveling sand into a box so that later [you] can build sandcastles’.  There is truth in all of these inspirational tips: first drafts are messy things: often incoherent in parts, full of both useful and useless information, lacking a proper focus. But, they are where we start any writing, and the key word here is ‘start’: writing is a non-linear, often chaotic, process, where we learn as we write, and our thinking develops with each round of feedback and revision.

This ‘logic of discovery’ is at odds, though, with the ‘logic of dissemination’ that we display in our finished thesis: the iterative process that produces the thesis is hidden from the view of the reader, as they are presented with our neat, polished, coherent argument. Many postgraduate students start their thesis process believing that these two logics are the same: that you start with Chapter 1, and the process unfolds neatly and logically from there. They become frustrated, then, when this turns out to be a lie:  when the truth is multiple drafts and mistakes, time spent writing paragraphs or pages of writing that have to be deleted when they are no longer relevant, and sometimes unexpected changes to your research questions, theory or methodology as the project evolves. This frustration can breed self-doubt if not carefully managed through supervision: many students believe that the more drafts you have to write, the worse you are as a writer; so many students I have met erroneously believe that the best writers don’t write that many drafts, and don’t make that many mistakes or revisions.

The opposite is the truth. The more successful writers, and postgraduate students, have learned to embrace the chaos and the frustration; they have learned to manage a balance between having a clear research plan and letting that process evolve so that they can still be surprised by what they find, or learn, as they write and work the data. This is a hard thing to do, live in a space where you know probably less than you don’t know, and where you have to be okay with the not-knowing, and move willingly between knowing and not-knowing over and over as your research moves forwards. This requires not just mental fortitude, but emotional resilience.

Researching and writing a thesis feels, at times, as if you are on a many-roaded route, trying to keep your eye on the GPS when it’s giving you more than one possible route and asking you to choose the best one to get you to your destination within minimal traffic and in good time. You may choose one route, and then find halfway you’ve made an error in judgement, and then choose to turnoff, and take a back road back to the main route you were on. There may be unexpected detours that the GPS didn’t know about and so couldn’t warn you of. You may feel like you are going around in circles at some points, and in a lovely, free-flowing straight line at others. A research degree, especially a PhD, represents a long road, with several possible routes to your destination. And it’s not a straight line. You may have to re-drive parts of the route at times, or try out different parts of the route than you expected to. But, if you try to trust the process, and make peace with taking your time and living with a bit of mess and non-linear chaos, you will hopefully get to your destination in one piece, and with a really good understanding of the area you’ve been driving around and around.

In research terms, this means getting more comfortable with the iterative nature of research, writing, and thinking. You cannot expect to write a chapter once, and be done. And you can’t expect to read something once and fully understand it, especially if it’s pivotal to your project, like theory. Writing multiple drafts, making mistakes, including knowledge and reading you don’t need along with that which you do, and making revisions that improve your writing, further your thinking and push your research forward is part and parcel of valuable, challenging postgraduate study that makes you a more capable researcher. Doing worthwhile research that pushes your field forward will require you to have a really firm understanding of that field, and the place your research can occupy within it. This means getting a bit lost sometimes, but having the means (through supervisors, peers, reading) to find your way onto your route again.

Terry Pratchett’s soundbite on first drafts is my favourite: ‘The first draft is just you telling yourself the story’. If you see your thesis as a story, evolving as the characters and plot take shape, and as the twists and turns reveal themselves through working with theory, methodology, data and analysis, it can be easier to embrace that uncertainty, and iterative rounds of writing, feedback, revision, and rewriting that push your research, and you as a researcher, forward. You start by telling yourself, and move to telling your supervisors, examiners and finally your wider audience – and you make a contribution that is valued and relevant. It won’t happen in a nice, linear way, but the depth of knowledge you gain, of your field and the research process, will be worth all the ‘driving’ in the end.

Iterativity in data analysis: part 2

This post follows on from last week’s post on the iterative process of doing qualitative data analysis. Last week I wrote a more general musing on the challenges inherent in doing qualitative analysis; this week’s post is focused more on the ‘tools’ or processes I used to think and work my way through my iterative process. I drew quite a lot on Rainbow Chen’s own PhD tools as well as others, and adapted these to suit my research aims and my study (reference at the end).

The first tool was a kind of  ’emergent’ or ‘ground up’ form of organisation and it really helps you to get to know your data quite well. It’s really just a form of thematic organisation – before you begin to analyse anything, you have to sort, organise and ‘manage’ your mountain of data so that you can see the wood for the trees, as it were. I didn’t want to be overly prescriptive. I knew what I was looking for, broadly, as I had generated specific kinds of data and my methodology and theorology were very clearly aligned. But I didn’t really know what exactly all my data was trying to tell me and I really wanted it to tell its story rather than me telling it what it was supposed to be saying. I wanted, in other words, for my data to surprise me as well as to show me what I had broadly hoped to find in terms of my methodology and my theoretical framework.  So, the ‘tool’ I used organised the data ‘organically’ I suppose – creating very descriptive categories for what I was seeing and not trying to overthink this too much. As I read through my field notes, interview transcripts, video transcripts, documents, I created categories like ‘focusing on correct terminology’ and ‘teacher direction of classroom space’ and ‘focus on specific skills’. The theory is always informing the researcher’s gaze, as Chen notes in her paper (written with Karl Maton) but to rush too soon to theory can be a mistake and can narrow your findings. So my theory was here, underpinning my reading of the data, but I did not want to rush to organise my data into theoretical and analytical ‘codes’ just yet. There was a fair bit of repetition as I did this over a couple of weeks, reading through all my data at least twice for each of my two case studies. I put the same chunks of text into different categories (a big plus of using data software) and I made time to scribble in my research journal at the end of each day during this this process, noting emerging patterns or interesting insights that I wanted to come back to in more depth in the analysis.

An example of my first tool in action

An example of my first tool in action

The second process was what a quantitative researcher might call ‘cleaning’ the data. There was, as I have noted, repetition in my emergent categories. I needed to sort that out and also begin to move closer to my theory by doing what I called ‘super-coding’ – beginning to code my data more clearly in terms of my analytical tools. There were two stages here: the first was to go carefully through all my categories and merge very similar ones, delete unnecessary categories left over after the merging, and make sure that there were no unnecessary or confusing repetitions. I felt like the data was indeed ‘cleaner’ after this first stage. The second stage was to then super-code by creating six overarching categories, names after the analytical tools I developed from the theory. For example, using LCT gave me ‘Knowers’, ‘Knowledge’, ‘Gravity’ and ‘Density’. I was still not that close to the theory here so I used looser terms than the theory asks researchers to use (for example we always write ‘semantic gravity’ rather than just ‘gravity’). I then organised my ‘emergent’ categories under these headings, ending up with two levels of coded data, and coming a step closer to analysis using the theoretical and analytical tools I had developed to guide the study.

By this stage, you really do know you data quite well, and clearer themes, patterns and even answers to your questions begin to bubble up and show themselves. However, it was too much of a leap for me to go from this coding process straight into writing the chapter; I needed a bridge. So I went back to my research journal for the third ‘tool’ and started drawing webs, maps, plans for parts of my chapters. I planned to write chunks, and then connect these together later into a more coherent whole. This felt easier than sitting myself down to write Chapter Four or Chapter Five all in one go. I could just write the bit about the classroom environment, or the bit about the specialisation code, and that felt a lot less overwhelming. I spent a couple of days thinking through these maps, drawing and redrawing them until I felt I could begin to write with a clearer sense of where I was trying to end up. I did then start writing, and working on the chapters, and found myself (to my surprise, actually) doing what looked and felt like and was analysis. It was exciting, and so interesting – after being in the salt mines of data generation, and enduring what was often quite a tedious process of sitting in classrooms and making endless notes and transcribing everything, to see in the pile of salt beautiful and relevant shapes, answers and insights emerging was very gratifying. I really enjoyed this part of the PhD journey – it made me feel like a real researcher, and not a pretender to the title.

One of my 'maps'

Another ‘map’ for chapter writing

A different 'map' for writing

A ‘map’ for writing

This part of the PhD is often where we can make a more noticeable contribution to the development, critique, generation of new knowledge, of and in our fields of study. We can tell a different or new part of a story others are also busy telling and join a scholarly conversation and community. It’s important to really connect your data and the analysis of it with the theoretical framework and the analytical tools that have emerged from that. If too disconnected, your dissertation can become a tale of two halves, and can risk not making a contribution to your field, but rather becoming an isolated and less relevant piece of research. One way to be more conscious of making these connections clear to yourself and your readers is to think carefully about and develop a series of connected steps in your  data analysis process that bring you from you data towards your theory in an iterative and rich rather than linear and overly simplistic way. Following and trying to trust a conscious process is tough, but should take you forward towards your goal. Good luck!

keep calm

 

Reference: Chen, T-S. and Maton, K. (2014) ‘LCT and Qualitative Research: Creating a language of description to study constructivist pedagogy’. Draft chapter (forthcoming).

 

Iterativity in data analysis: part 1

This post is a 2-parter and follows on from last week’s post about generating data.

The one thing I did not know, at all, during my PhD was that qualitative data analysis is a lot more complex, messy and difficult than it looks. I had never done a study of this magnitude or duration before, so I had never worked with this much data before. I had written papers, and done some analysis of much smaller and less messy data sets, so I was not a c0mplete novice, but I must say I was quite taken aback by the mountain of data I found I had once the data generation was complete. What to do now? Where to start? Help!

The first thing I did, on my supervisor’s advice, was get a license for Nvivo10 and uploaded all my documents, interview and video recordings and field notes into its clever little software brain so that I could organise the data into folders, and so that I could start reading and coding it. This was invaluable. Software that enables you to store, organise and code your data is a must, I think, for a study as large and long as a PhD. This is not an advert for Nvivo so I won’t get into all its features, and I am sure that other free and paid-for qualitative data analysis packages like Atlas Tii or the Coding Analysis Toolkit from UMass would do the job just as well. However, I will say that being able to keep everything in one place, and being able to put similar chunks of text into different folders without mixing koki colours or scribbling all over paper to the point of confusion was really useful. I felt organised, and that made a big difference to my mental ability to cope with the data analysis and sense-making process.

The second thing I did was keep very detailed notes in my research journal on my process as it unfolded. This was essential as I needed to narrate my analysis process to my readers in as much detail as possible in my methodology chapter. If a researcher cannot tell you how they ended up with the insights and conclusions they did, it is much harder to trust their research or believe what they are asking you to. I wanted to be believable and convincing – I think all researchers do. Bernstein (2000) wrote about needed two ‘languages of description (LoD)’ in research: the internal (InLoD) which is essentially where you create a theoretical framework for your study that coheres and explains how you are going to understand your problem in a more abstract way; and the external (ExLoD) where you analyse and explain the data using that framework, outlining clearly the process of bringing theory to data and discovering answers to your questions. The stronger and clearer the InLod and ExLoD, the greater chance other researchers then have of using, adapting, learning from your study, and building on it in their own work. When too much of your process of organising, coding, recoding, reading, analysing, connecting the data is hidden from the reader, or tacit in your writing about it, there is a real risk that your research can become isolated. By this I mean that no one will be able to replicate your study, or adapt your tools or framework to their own study while referencing yours, and therefore your research cannot be readily be built on or incorporated into a greater understanding of the problems you are interested in solving (and the possible solutions).

This was the first reason for keeping detailed notes. The second was to trace what I was doing, and what worked and what did not so that I could learn from mistakes and refine my process for future research projects. As I had never worked with a data set this large or varied before, I really didn’t know what to do, and the couple of qualitative research ‘textbooks’ I looked at were quite mechanical or overly instrumental in their approach, which didn’t make complete sense to me. I wanted a more ‘ground-up’ process, which I felt would increase the validity and reliability of my eventual claims. I also wanted to be surprised by my data, as much as I wanted to find what I thought I was looking for. The theory I was using further required that I not just ‘apply’ theory to data (which really can limit your analysis and even lead to erroneous conclusions), but rather engage in an open, multiple and iterative reading of the data in successive stages. Detailed notes were key in keeping track of what I was doing, what confused me, what made sense and so on. Doing this consciously has made me feel more confident in taking on similarly sized research projects in future, and I feel I can keep building and learning from this foundation.

This post is a more conceptual musing about the nature of qualitative data analysis and lays the groundwork for next week’s post, where I’ll get into some of the ‘tools’ or approaches I took in actually doing my analysis. Stay tuned… 🙂