Tag: theoretical frameworks

Why is theory such a big deal in postgraduate research?

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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.

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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. 

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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.

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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.

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

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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.

Building a theoretical framework for your study

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I am presenting a seminar tomorrow to PhD students on how I developed my PhD’s theoretical framework. When I agreed to do this workshop last year, I thought this would be fairly easy to do. However, I am finding it difficult to articulate the differences between substantive theory and what I think of as ‘framework theory’, and how to use both in different ways to contextualise your study and build a framework for it. This was, for me, the first and biggest ‘threshold’ (to use Meyer and Land’s term) that I crossed in my own PhD, and it’s a very important one.

There are two main places you tend to use theory in a social sciences PhD (I’d be really interested to hear about the differences between these and PhDs in the natural sciences): in your literature review where you contextualise your study and the rationale for it, and in your theoryology, as I call it, which works at a more ‘meta’ level to discuss how you understand the questions you are researching, how you plan to approach the research, and how you will move from theory to methodology and methods, and on to analysis. You come back to the theory in your conclusion, connecting it to your findings, but it needs to be laid out ahead of this in the earlier chapters.

I have written about writing a literature review, and using the research in the field in a more substantive way to build the context and rationale for your own study, and situate it within your field, so I won’t focus too much on that in this post. Here, I would rather focus on the more difficult-to-articulate issue: the theoretical framework. The reading PhD students almost always start with is the substantive ‘theory’ and research. You need to know what research has been done in your field, what the key issues and questions are, where the field is heading, and where you, coming into that field, can place your own research. But you may well find, if you spend long enough reading the substantive theory and research, that you start going in circles, confirming what you have already found out and slowly getting stuck. This is when you need to take a break. If you know what the lie of the land is, and you have a grasp of the trends, issues and research problems that are being or have been researched, and where the gaps you, you now need to find your question, your project. You should find the topic you want to research, and the focus, from your substantive reading, but you need to hone this to turn it into a lightning rod for all the other research and reading you will do to cling to. In my (albeit limited) experience, moving on to the ‘meta’ stuff at this point helped me to do this.

What do I mean by ‘meta’ stuff? The Free Dictionary defines metatheory as ‘a formal system that describes the structure of some other system, and ‘a theory devised to analyse theoretical systems’. Slightly obscure definitions on their own – theories that can analyse theories sounds like more going round in circles. But I’m going to concentrate on the words ‘formal system’, ‘structure’, and ‘analyse’ because this, for me, is what a theoryology hinges on. What comes after you  have focused on one research question your study can explore and answer? You need to design a methodology, and then use appropriate research methods to generate data, and then employ an analytical framework to organise that data. Thereafter, you need tools of analysis to make sense of the data in relation to the theoretical structure you have put together, and the field in which your research is located. All of this is nigh on impossible to do in any coherent, structured and organised way without an organising structure that can hold the project in place and on course. You need, then, a formal system, a structure, that can inform the methods you use, your data generation plans and strategies, and the ways in which you then organise and analyse the data to answer your research question. This formal system is your theoretical or conceptual framework (your theoryology).

But how do you build one? What kind of theory is theoryology-worthy? Theory is a weird word here, and it is often misused. I am sure I misuse it all the time. Often, when we say ‘theory’ we mean ‘other research that has been done in the field that informs how I think about my own research and/or practice’. By metatheory is meant something that, while derived from empirical research and data, is raised a bit further above the empirical to create a more generalised or abstract set of principles that can be applied as a formal organising or structural system in a range of studies. Theoretical frameworks are built out of this kind of theory. You need an abstract, generalised set of principles that you can adapt and apply to your own study, and that will inform what data you choose to generate, and how you then can organise that data and decide on methods of analysis. A helpful way of finding your way to this kind of theory that might be right for your study is to look at what kinds of frameworks or conceptual tools other researchers are using in your field or allied fields.

Finding a framework for your study is essential in the social sciences, certainly. Without an overarching metatheory to organise and analyse your project you may end up generating a mountain of data that you just get lost in because you’re not sure what is important or what is not; you may end up with the wrong kinds of data; or you may write a ‘findings’ chapter that describes rather than analyses the data in relation to the field itself, and the question your are answering, which would leave you in a precarious position, given the pressure on PhDs to make new contributions to knowledge. Don’t be too worried if this process of developing your own framework takes time (I spent most of my second year on mine). In my experience, if you have a solid framework that is chosen well and clear to you, what follows is generally more organised and less fraught on the whole*.

*Of course, all sorts of things can and do go wrong in a PhD, and this is just my experience, but colleagues who have followed this route have found similarly that the data generation and analysis process has been less stressful if they have a clearer sense of what they are looking for, why, and how to look at it.

Disciple or pathmaker? Critiquing your theoretical ‘gurus’

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I recently enjoyed an amazing week at the University of Sydney, where I spent time talking about my work with the researcher who developed the conceptual tools I used to frame my theoryology and methodology, as well as with PhD scholars and researchers who are also using these tools in their own research. Two of my friends asked me whether the week with my ‘guru’ was a good one, and these comments coupled with a recent seminar I attended on data and theory gave me pause for thought. I do not think of this person as my ‘guru’, although I find the tools truly brilliant, and I am so enjoying working with them in my research and practice. I think (I hope) I am capable of putting aside my admiration for both him and this conceptual toolkit to offer critique and questions, and to find ways in which I can contribute to this field of research and practice with my own work. But, when you are new to a particular theoretical or conceptual approach, or new to a significant piece of research like a PhD which requires so much deep and extended engagement with theory, it can be really difficult to do this. I certainly found it difficult to be critical of the concepts when I was working with them during the PhD. This ability I now feel I have to critique the tools and the theory is very much a post-PhD thing.

Why are we so tempted to make ‘gurus’ out of our favourite theorists? Why is it hard to be critical of the theories we have chosen to use to provide the foundation and analytical tools for our research, especially in a PhD? I think, for me, the answer was quite simple: because I needed them to be right. I really wanted to have answers to my questions, and I really needed the theory and the tools to provide me with those answers. I suppose, too, I was afraid that pointing out gaps, holes and areas where the theory was still fuzzy would ultimately weaken my stance and my arguments. I opted, without quite realising it at the time, for being a bit of a theory disciple. This was in big part due to what I have just said, but it was also due to the fact that I was so excited about the ways in which the theory opened my eyes to things in the environments I was researching that I had not been able to see otherwise, or in quite those ways, that I got a bit carried away by just how thrilling my research actually was for me when I got into the data  and started finding tentative answers to my questions.

One of my thesis examiners suggested to me in the report I received that I should exercise caution in getting too excited and too carried away. Part of the role of a good researcher is to be able to stand back a bit from the thing we are looking at, or the lenses we are looking through or with, and wonder if we are seeing the right sorts of things, or asking the right sorts of questions. We need to be able to see gaps, holes, inconsistencies, not just to avoid being accused of having a weak argument and having no defence in your viva or examination, but more importantly to show that you are clear enough on what theory you are using, as well as why and how you are using it, that you can show that although there may be questions you cannot yet answer, or things you cannot yet see, you know that the answers you have are good ones, even though they are always partial and fallible. You aren’t answering all the questions in your PhD – you are just answering one – but you need to be able to show not just all the reasons why your theoretical framework and tools are right for answering this question; you also need to be able to be critical and careful, so that you anticipate and can stand up to critique of your own work and answer back to the critics.

Rather than being theory disciples, I think we should be aiming to be research pathmakers. Theory on its own is a bit pointless. Your research will bring any of the theory you use to life in a range of ways depending on how you draw the framework, and also what data you choose to generate, analyse and interpret using the theory. You can be a pathmaker, even if the bit of path you are chipping out for others to walk on is small or short. But, this is not easy. I think you have to be well-read and brave to be critical, and you also need to know your theoryology well. It is almost impossible to offer a sound and useful (but not too damning) critique of your own theoretical framework unless you really know it well. In chipping out your piece of path, you will be following the paths of those who have gone there before you, and you’ll hopefully be either extending the paths they have created, or branching off in slightly new and unexplored directions, rather than simply smoothing out their already-trod path or pruning the bushes on either side of it :). It’s hard to be this kind of pathmaking researcher if you are not going to be brave enough to take your ‘gurus’ off the pedestals you could tend to place them on when you start your PhDs, and offer thoughtful, relevant and useful critique that shows your readers just how well you do know your own field, and that adds depth and credibility to your own researcher’s voice.

I have learned that I don’t need the theory or my answers to be ‘right’ (if they even can be); but I do need them to be credible, productive and interesting, and being able to both believe in and offer measured critique of the theoryology that serves my research well will certainly help me to find my ways to the kinds of answers I am seeking.