Why is theory such a big deal in postgraduate research?

I am working with a new student. Long story short, I am not his first supervisor, and this his not his first attempt at his PG research project. He’s had a tough time thus far: significantly with theory as his first supervisor did not seem to feel he needed any. Quite understandably, then, one of his first questions to me was ‘why are we making such a big deal about theory [when my research is narrative]?’ In answering this question, I have been pondering a bit more about why theory is such a big deal in research, especially at PG level.

The best way to begin is with an overview of what postgraduate research (any academic research perhaps) is for: to make a novel, valuable and needed contribution to knowledge in your field or study and/or practice. Often, particularly in the social sciences, we are taking a known problem and trying to solve it with a new approach, or we are critiquing the work of others from a particular perspective to extend knowledge further, or we are introducing a new problem, solvable with established approaches in ways that extend or consolidate knowledge and practice. To achieve this contribution to knowledge, we focus on small slice of the known world – our data – and we analyse this in ways that connect our findings to broader understandings/knowledge/phenomena so that what we are contributing clearly fits within the bigger picture in our field. 

If this, then, is basically why we do research, then how do we actually achieve this goal of saying something new and fitting the new into the established knowledge in our field? This is, in many instances, where theory really does its best work.

leaving star trek GIF-downsized_large

When we do academic research, any research, we are trying to find an answer to a question that needs one. We start with a research problem, and we read around that, becoming increasingly focused until we have read enough to locate a gap in the field that we can contribute to filling with our research. We then narrow down a research question, the answers to which will fill (part of) this gap. At this point, we have a sense of what data we are going to generate and how (research design and method) and we may even (from reading) have a basic sense of what we may find. But, what we need is a framework within with to understand what we may find, and tools to use to make meaning from this data. We need to ensure that we move beyond purely descriptive meanings, even in descriptive studies. If all we are doing is describing or narrating our small slice of the world, it may be interesting, but perhaps only to a tiny group of potential readers who understand the specifics well enough to extract meanings of their own. This falls short of the kinds of contribution to knowledge expected of postgraduate scholars and publishing academics.

The potentially frustrating and difficult issue of finding the right framework for your research is that you can’t really ‘find’ one and just put it into your project, where it will do its own thing. Doing this would be akin to writing a ‘theory’ chapter or section, and then doing nothing with that theory in the analysis to connect your study to the field. Rather, you have to build and use your theoretical framework to make sense of your study, and its contribution to the field. This means you need to find theory that fits with your research problem and questions, that can help you understand this problem in helpful ways. Then, you need to select the relevant parts of the whole theory (you don’t necessarily, for example, need to include everything Pierre Bourdieu ever wrote in your thesis if all you really need to focus on is the interplay between capital and habitus in the structuring of a field). This selected theory then needs to be explained, exemplified in relation to your study, and connected into a coherent structure, or framework. 


scrabble mess

Once you have what Bernstein called the ‘internal language of description’ for your study – your study’s own account of the theory it will be using and why this theory is the most appropriate choice for this study – you can generate, or analyse generated, data. This is where theory becomes the big deal that it is. Theory is transformed when it is brought into contact with data. It stops being quite so abstract, and becomes more alive and real. It actually helps you to say something about why you see what you do in your data, and what the things you see actually could mean, connected to the larger picture. It helps you create an ‘external’ language of description – a translation device as Maton puts it – which transforms theory in the abstract into an analytical language that can describe and make meaning of data. Other researchers can draw on, adapt, and add to this in their own studies, further amplifying the value of your research.

For example, several students have told you that no one will assist them with supervisor issues. rather than saying that this is just an unsupportive environment, you can use theory that gives you insight into power and university cultures around autonomy. With this insight, you could postulate that the environment is structured so as to give administrators and supervisors way more power than students, and with that power they can maintain an unsupportive status quo. Perhaps this unsupportive environment is created and maintained with the (misguided) notion that students need to be autonomous and independent, but you can now critique this with your data and theory to show why this doesn’t actually work. And you could back up this postulation with reference to other studies that have made similar or related arguments.

Instead of just a small story about your data, and why you think it is interesting, you now have a potentially powerful analysis of the data that says what is means, why this meaning is important to pay attention to, and how this meaning connects with other meanings, thus making a contribution to research in your field.


Theory isn’t just an odd requirement that has to be met in postgraduate research. It also is not some sort of relic of an ‘elitist’ version of higher education (one criticism I have heard a few times now). It’s a tool: it helps us really say something important and valuable about the world around us. We need to be doing research that connects us to other people, other research, other meanings, so that all of these meanings and arguments can build on one another cumulatively, amplifying our findings and voices. If what we want is better understanding of problems, new solutions to old problems and powerful change, then we need to harness the power theory offers us as researchers and use it to help us achieve these goals.


Putting your theory to work in analysis

You now have generated data – in some form, whether primary or secondary – and now you need to code and make sense of it; you need to put it to the task of answering your research question(s). In other words: analysis. This was the toughest part of my own PhD: I had a mountain of data – how to choose the right pieces? What to say about them? How to make sense of them in relation to my research questions?

This is where theory and concepts come into their own in a PhD or MA. You will have some form of theoretical or conceptual framework (for clarity on theory and concepts, how they differ and work together, please watch this short video). Where students often go off track, though, is not using these concepts or theory to do the work in analysis. The theoretical or conceptual framework ends up standing alone, and some form of thematic description of the data is made, with a rather thin version of analysis. In this situation, it may be difficult to offer a credible answer to your research question.

Analysis is, in essence, an act of sense-making. It requires you to move beyond a common sense, everyday understanding of the world, and your data – the level of the descriptive – to a theorised, non-common sense understanding – the level of the analytical (and critical). Analysis means connecting the specific (your study and its data) with the general (a phenomenon, theory, concept, way of looking at the world) that can help to explain how the specific fits in with, or challenges, or exemplifies the general. If you do not make this move, all you may end up with is a set of data that describe a tiny piece of the world, but with little or no relevance to anyone else’s research except perhaps the few other people researching the same thing you are.

theory specs 2

So, how might you ‘do’ analysis?

Imagine you are doing a study on the role of reflective learning in building students’ capacity to critique and create professional knowledge that encourages ongoing learning and problem-solving. ‘Reflection’, or ‘reflective practice’ would be a key concept, as would ‘professional knowledge’, ‘problem-solving’, and ‘learning’. These have generalised, or conceptual meanings that could apply in a range of ways, depending of the parameters and questions of a specific study. Thus, they can do analytical work, helping you to theorise as you answer your research questions.

Then imagine your data set is assessment tasks completed by students in social work and accounting, as two professional disciplines which require adaptive, ongoing learning and problem-solving. You now need a way of employing your key concepts in analysis. You could look at the intentions of the task questions – how they do, or do not, explicitly or actively enable or encourage problem-solving and reflective thinking and learning, and then look at students’ responses and see the extent to which the desired forms of learning are visible or not. This would yield useful findings to feed back to these disciplines in using assessment more effectively.

To reach theorised findings that go beyond describing what the tasks and the student writing said, and conjecture about what the tasks and written responses mean in relation to your study’s understanding of professional knowledge, learning, problem-solving and reflection, you need to start with questions.

theory giphy

For example: these tasks seem to be using direction words such as ‘name’, ‘list’, ‘describe’, ‘mention’, which require mainly memorising, or learning the notes in a rote manner. What kind of learning would this encourage? What impact would this have on students’ ability to move on to more analytical tasks? Is there a progression from ‘memorisation’ towards ‘problem-solving’ or using knowledge to reflect on and learn from case studies etc? What kind of progression is there? Is it sensible, or not, and how could this affect students learning? And so on.

You could then present the data: e.g., this is the task, and this is when students work on this task in the semester or progression of the course, and this is the task that follows (show us what these look like by copying them out, or including photographs). This part of the analysis is quite descriptive. But then you pose and answer relevant questions guided by your overall research objectives: if these two disciplines – social work and accounting – require professional learning and knowledge that is built through reflection, and the capacity to USE rather than just KNOW the knowledge in the field so that professional can adapt, continue learning, and solve complex problems, what kinds of assessment tasks are needed in higher education? Do the tasks students are doing in the courses I am studying here do these kinds of tasks? If yes, how are they working to build the rights kinds of knowledge, skills and aptitudes? If no, what might be the outcome for these students when they graduate and move into the professions? You then have to use the concepts you have pulled together to create a theorised understanding of professional reflective learning to pose credible answers, that are substantiated with your data (as evidence). This is the act of analysis.


In both qualitative and quantitative studies, the theory or concepts you choose, and the data you generate, are informed by your research aims and objectives. And in both kinds of studies, analysis requires moving beyond description to say something useful about what your data means in relation to the general phenomenon you are connecting with, and that informs your theorisation (student learning, climate change, democratic governance, etc). Thus, you need to work – iteratively and in incremental stages – to bring your theory to your data, to make sense of the data in relation to the theory so that your study can make a contribution that speaks both to those within your research space, and those beyond it who can draw useful conclusions and lessons even if their data come from somewhere else.


Concepts and theory: constructing a ‘gaze’ for your study

I have been thinking a great deal lately about theory, and the role it has to play in research. There are a couple of contexts in which this thinking has been taking place: I reviewed a paper recently that didn’t quite hang together, and after a second reading I worked out that I was missing the significance of the research, largely because the findings were not theorised, even implicitly. I then reviewed an MA proposal in which the student hinted at a particular body of theory in her literature review, but didn’t follow through with an explicit theoretical framework that she then connected to her proposed methodology and mode of analysis. I have also been reading and commenting on a student’s ‘theory chapter’ and have been thinking about how to help her build this theoryology so that it is fit for purpose as she moves into her data, and the analysis of it.

What is theory for?

All of these different ways in which theory, or concepts that are part of theories, have (or have not) been used in all this reading have brought me to one basic conclusion: the thing that theory does in our research is enable us to see the thing we are researching in a new, and hopefully more illuminated light. It enables us to lift ourselves out of the minutiae of what we are researching – the words our participants say, or write in documents, or the issues we are engrossed in while generating and coding data – and see patterns, and bigger contexts and questions. It also helps us to connect our research findings more clearly with the field we are researching in. Without any kind of theoretical or conceptual ‘gaze’ or way of seeing, I wonder if we can do research that adds to knowledge in our field in useful, clear and significant ways.

I do not think that all research needs to employ high-brow, complex or fancy theory – we don’t all need to be Foucauldian scholars, or read Heidegger, Deleuze or Bourdieu in their entirety (thank goodness!). I have worked with many postgraduate and undergraduate students who are scared of theory, because they conflate theory with complexity, and therefore with work that is too difficult and abstract to make sense of. This is a mistake, because theory is actually both useful, and necessary, in research. ‘Theoretical’ research is the wrong term, I think (unless we are actually doing the work of theory-building or theory-creating, which few of us are). What we are aiming for is ‘theorised’ research; research that does more than just describe what it sees, but goes beyond that to consider implications, significance and field-building.

Building the framework we need

Building-BlocksWe need, as researchers, to build a theoretical framework that will hold and guide our research, and that will help us to choose the most suitable methodology, data organisation and coding tools, and analytical tools as well. This is the foundation, in many ways, for our research. We need, within these frameworks, to select and connect concepts that help us (and our readers) to understand the part of the world we are researching clearly, and in a way that coheres both epistemologically and ontologically. In other words, we need to build, out of our chosen concepts, theoretical frameworks that make sense in the context of the study we are engaged in, and that help us to see more clearly the things we are researching, and say something new, interesting and useful about them. But we cannot just cherry-pick many shiny concepts that look and sound interesting, trendy or clever. We need to select carefully, to ensure, at a deeper level where we consider what we conceptualise as knowledge or truth and how that comes to be known, that we have agreement between the component parts we are building our framework with.

If you are, for example, an epistemic constructivist in terms of your understanding of what the world and knowledge about it entail and how we can come to know anything, you would not choose concepts for your framework from a critical or social realist school of thought, because at a deeper ontological and epistemological level, there would be disagreements that would be difficult to reconcile. Thus, you need to build your framework with a view to the epistemological and ontological underpinnings of the theories and concepts you are reading about and considering.

You also need to choose parsimoniously: how many concepts do you really need to build your framework? How much theory is enough for your problem, and your readers? Often, you can’t really know the answer until you have generated and analysed your data, so the theoryology you start off writing may be larger and more complex than you actually need it to be to tell the story of your research, its findings and their significance. You can and may well cut your theoryology post-analysis, trimming it to be as concise, clear and relevant as it needs to be within the context of your completed research.

However, even though your initial theory chapter draft is by no means final, try not to simply lump all the concepts you find interesting and helpful together in a long list, summarised and synthesised together. As you are writing this part of your thesis, think about the work the concepts you have chosen are doing for you. How do they connect with your research problem, and what relevance do they potentially have in this study? Your reader needs to be written into your theoretical gaze, so that when they come to read your methodology, and the findings and discussion thereof, they can see the theory coming through, and shaping and informing choices made and analyses offered.

etsy specsThere are, of course, different kinds of levels of theory – substantive, meta, applied and so on. I’m not sure I really understand all the differences, to be honest, but I think, regardless of the kind of theoretical framework you are building for the specific research problem you are investigating, it’s useful to remember that the role of theory is to provide you with principled insight: insight into the problem and context you are engaged in that helps you lift out of the murky mess of details, case studies or quantitative data and ask, for example:  ‘Why is this particular thing happening in this way? What could be causing this? How could this be addressed or solved or thought about in a new way? How could we address it, and what would the implications be? Theory gives you the means to go beyond your small research problem and think about it in a more principled, generalised way, so that rather than producing many small scale studies that cannot speak beyond specificities, we can produce research that uses local, smaller scale cases or data to build our collective knowledge about the issues, problems and solutions that have relevance within our spheres of interest, research and praxis.

Spinning the ‘golden thread’ that can sew your PhD together

When I was doing my PhD, someone at some stage asked me (probably in response to my ramblings about what my PhD was about): ‘what is your “golden thread”?’ This stumped me. My what? I hadn’t really heard that term before, although my supervisor has talked about it since, as have other colleagues who all supervise students – it seems to be a fairly common notion then, this notion of a ‘golden thread’ with which you can ‘sew’ your PhD thesis together. But what, indeed, is a golden thread, where do you get one, and how do you work out how to sew your PhD together?

To begin with what it is: the golden thread is, for want of a better explanation, the central argument that pulls through your whole thesis, and creates coherence across the literature review, the research questions, the theoretical and conceptual framework, the methodology, and finally the analysis and organisation of the data and the conclusions you are able to draw (on the basis of that argument you set out to make). It sounds quite straightforward when it is put like this, but in my experience (and in the experience of many other PhD students) it is really difficult to find and hold onto over the long course of researching and writing a PhD thesis. Another way of thinking about it would be to keep reminding yourself about what the point of your PhD is. What is it actually about – what are you trying to say here? A friend of mine types her main research question into the header of each page she works on in each of her chapters, so that she is not tempted to go off track in her writing and thinking; another friend wrote a haiku about the main point her PhD was making, and stuck it in a place she could see it when she was writing; another wrote her research questions on several sticky notes and put them above her desk at work and her desk at home, so that she had them in front of her whenever she was working on the thesis. I kept a fairly faithful research journal, and re-read it often, to remind myself what I was actually making my argument about.

So, how do you get one? Sadly, you cannot go to PhDarguments.com and order one; you have to make or build one, and this takes time and is really challenging. I think of it a bit like Rumpelstiltskin turning all the straw into golden thread (except without all the creepiness). What you have when you start a PhD is straw – ideas, concepts, theory, methods, questions, literature you have read – and you have to pull the right pieces of straw together to make a strong, shiny length of golden thread that you can then use to sew a beautifully coherent and persuasive PhD thesis. Like theoretical frameworks, analytical frameworks, literature reviews, an argument is built part by part and always in relation to the main question it is being made to answer. There are key parts of the thesis that you need to put into place as you go to help you create strong and coherent sub-arguments that build towards the overall, central argument your PhD will make.

You need to scope your field well, and find a gap into which your research could fit – this helps you to start asking more refined questions, which can turn into research questions. You need to move from this reading into tougher theoretical and conceptual territory – you need to find your theoryology, and with it, further refinement and focus of your research questions. You need then to consider how you will answer these questions: what data will you need? How will you find it? What will you do with it in order to make sense out of it, and select what is relevant to analyse in relation to your research questions? Then you need to further consider the research questions you are trying to answer as you connect the theory with the data in the process of analysing it, and using it to tell the story that answers your questions, and explains why both the questions and the answers are important to your readers, and your research community or field. Following a logical and coherent process, and pulling each part of the process through with you into the subsequent stage or part of the process, really helps. In other words, don’t leave all your theory and research questions behind when you plan out your methodology and generate your data. Don’t forget the scoping of the field you have done, the research questions you are asking, and your theoretical framework and conceptual tools when you organise and begin to analyse that data in order to build your strong, shiny argument.

Image from uklpf.co.uk

Image from uklpf.co.uk

The argument, in the end, is the thing with the PhD. You cannot have your readers get to the end of it wondering: ‘So what? Why did I just read all of that? What was the point?’ The golden thread is just that: the answer to the ‘so what’ question; the point of the research; the central argument you have made on the basis of the research you have done. Without it you don’t have a PhD thesis; you have parts of a whole that has not been realised or pulled together. In order to sew those parts into something that represents what Trafford and Leshem have termed ‘doctorateness’, you need to channel Rumpelstiltskin, and start turning all your straw into your own golden thread, so that you can sew the parts of your research into a coherent, persuasive, strong PhD thesis.

‘Retrofitting’ your PhD: when you get your data before your theory

I gave a workshop recently to two different groups of students at the same university on building a theoretical framework for a PhD. The two groups of students comprised scholars at very different points in their PhDs, some just starting to think about theory, some sitting with data and trying to get the theory to talk to the data, and others trying to rethink the theory after having analysed their data. One interesting question emerged: what if you have your data before you really have a theoretical framework in place? How do you build a theoretical framework in that case?

I started my PhD with theory, and spent a year working out what my ‘gaze’ was. I believed, and was told, that this was the best way to go about it: to get my gaze and then get my data. In my field, and with my study, this really seemed like the only way to progress. All I had starting out was my own anecdotal issues, problems and questions I wanted answers to, and I needed to try and understand not just what the rest of my field had already done to try and find answers, but what I could do to find my own answers. I needed to have a sense of what kinds of research were possible and what these might entail. I had no idea what data to generate or what to do with it, and could not have started there with my PhD. So I moved from reading the field, to reading the theory, to building an internal language of description, to generating data, to organising and analysing it using the theory to guide me, to reaching conclusions that spoke back to the theory and the field – a closed circle if you will. This seems, to me certainly, the most logical way to do a PhD.

But, I have colleagues and friends who haven’t necessarily followed this path. In their line of work, they have had opportunities to amass small mountains of data: interview transcripts, documents, observation field notes, student essays, exam transcripts and so forth. They have gathered and collected all of these data, and have then tried to find a PhD in the midst of all of it. They are, in other words, trying to ‘retrofit’ a PhD by looking to the data to suggest a question or questions and through these, a path towards a theoryology.

Many people start their doctoral study in my field – education studies – to find answers to very practical or practice-based questions. Like: ‘What kinds of teaching practice would better enable students to learn cumulatively?’ (a version of my own research question) Or: ‘What kinds of feedback practices better enable students to grow as writers in the Sciences?’ And so on. If you are working as a lecturer, facilitator, tutor, writing-respondent, staff advisor or similar, you may have many opportunities to generate or gather data: workshop inputs, feedback questionnaires, your own field notes and reports, student essays and exam submissions, and so on. After a while, you may look at this mountain of data and wonder: ‘Could there be a thesis in all of this? Maybe I need to start thinking about making some order and sense out of all of this’. You may then register for a PhD, searching for and finding a research question in your data, and then begin the process of retrofitting your PhD with substantive theory and a theoryology to help you work back again towards the data so as to tell its story in a coherent way that adds something to your field’s understanding or knowledge of the issues you are concerned with.

The question that emerged in these workshops was: ‘Can you create a theoretical framework if you have worked so far like this, and if so, how?’ I think the answer must be ‘yes’, but the how is the challenging thing. How do you ask your data the right kinds of questions? A good starting point might be to map out your data in some kind of order. Create mind-maps or visual pictures of what data you have and what interests you in that data. Do a basic thematic analysis – what keeps coming up or emerging for you that is a ‘conceptual itch’ or something you really feel you want or need to answer or explore further? Follow this ‘itch’ – can you formulate a question that could be honed into a research question? Once you have a basic research question, you can then move towards reading: what research is being or has been done on this one issue that you have pulled from your data? What methodologies and what theory are the authors doing this research using? What tools have they found helpful? Then, much as you would in a more ‘traditional’ way, you can begin to move from more substantive research and theory towards an ontological or more meta-theoretical level that will enable you to build a holding structure and fit lenses to your theory glasses, such that you have a way of looking at your data and questions that will enable you to see possible answers.

Then you can go back to your data, with a fresh pair of eyes using their theory glasses and re-look at your data, finding perhaps things you expect to see, but also hopefully being surprised and seeing new things that you missed or overlooked before you had the additional dimension or gaze offered by your theoretical or conceptual framing. But working in this ‘retrofitted’ way is potentially tricky: if you have been looking and looking at this data without a firm(ish) theoretically-informed or shaped gaze, can you be surprised by it? Can you approach your research with the curious, tentative ‘I don’t know the answers, but let’s explore this issue to find out’ kind of attitude that a PhD requires? I think, if you do decide to do or are doing a PhD in what I would regard as a middle-to-front sort of way, with data at the middle, then you need to be aware of your own already-established ideas of what is or isn’t ‘real’ or ‘true’, and your own biases informed by your own experience and immersion in your field and your data. You may need to work harder at pulling yourself back, so that you can look at your data afresh, and consider things you may be been blind to, or overlooked before; so that you can create a useful and illuminating conversation between your data and your theory that contributes something to your field.

Retrofitting a PhD is not impossible – there is usually more than one path to take in reaching a goal (especially if you are a social scientist!) – but I would posit that this way has challenges that need to be carefully considered, not least in terms of the extra time the PhD may take, and the additional need to create critical distance from data and ‘findings’ you may already be very attached to.