Having theory and ‘theorising’: reclaiming the verb

Jeepers, I have been away a while! But, rather than dwelling on all the Things I let get in the way of my writing and creativity last year, I will look to a new year, and new ideas for the blog instead. In a conference workshop in November, I had a really interesting discussion with colleagues about theory – where theory comes from, and whether its origins are relevant to its current use (for a later post). Part of that discussion focused on how we use theory. It is this I would like to focus on in this post, to kick off the new year.

Theory is key part of research – particularly at postgraduate and doctoral level. In the social sciences we tend to really spell out the theory we use – critical social theory, behavioural theory, post-structural theory and so on, whereas is the natural sciences there is less obvious mention of theories although they are most certainly there (I am told by scientist friends it is usually some version of positivism, but I stand to be corrected). In essence, theory is always present when we are trying to make sense of a smaller part of the bigger picture – connecting the specifics of our study with a more general or wider phenomenon. But, the way we use theory to do this is often inadequately talked about or grappled with in postgraduate spaces. Do we ‘have theory’ or do we ‘theorise’? 

question mark

Patrick White, in an excellent book on research writing, argues that to be called by such a name, theories need to be abstract, explanatory and testable. In other words, they need to be able to apply across more than one field; they need to explain, rather than just name, the part of the world we are researching; and they need to be able to be tested, to see whether and how they apply to the problem we are researching. But, in many projects, especially those where the theory needs to shape choices made in the research design, methods, and analysis of data, theory can be under-utilised in the act of making meaning and creating new knowledge. In other words, I am arguing that there are projects that ‘have theory’, but this is not adequately used in the act of ‘theorising’ new understandings and explanations of the problems we research. 

So what, then, do I mean in my distinction between ‘having theory’ and ‘theorising’?

Let’s start with theorising. This is an act – something we have to do, usually in an iterative manner. This is the act of bringing theory and data into conversation with one another, in the process seeking meanings that will help us both explain the nature of the problem we are researching, and test the viability of the theory we have chosen in making sense of that problem. We are also locating the problem, through theory, literature and methodology, within the bigger picture it connects with. For example, using critical social theory, such as Margaret Archer’s morphogenetic cycles to critique and understand processes of change or stasis in one university’s leadership environment (understood within the broader context of neoliberalism and marketisation of higher education). To theorise, we need to make the theory do work. The work of making sense of the world – through a chosen and always partial lens – but making sense nonetheless.

When we use theory to theorise – to seek, debate and construct meanings so as to create and add to knowledge about the part of the world we are researching – we need to acknowledge that the theory we have chosen is but one of probably a number of theories that could offer an explanation. Research is about looking for truth – a form of truth that enables us to see, know and act with greater knowledge and insight. Thus, the theory we choose, following White, must be able to offer the kinds of explanations that enable this. Your research question – the problem you want to solve – must guide the choices you make here: what theoretical tools, frameworks, explanations will enable you to solve this problem in the clearest, most useful way at this point in time? We choose, and build, theoretical frameworks, or theoryologies as I have called them, based on the problems we are trying to solve, or the truths we seek. They are not just found, fully formed, ready to be placed into the right part of the thesis. 

constructing theory

Photo by David McBee from Pexels

This brings me to dissertation work that has theory, but does not fully theorise. There may be a chapter entitled ‘Theoretical framework’ or some version of that, and the theory is laid out and explained. (In the sciences the theory may be implied in the earlier parts of the thesis rather than explained in its own chapter.) Yet, when the analysis and data sections are reached, the theory is oddly silent, or under-utilised. Rather, you may find a thematically organised discussion of the data that recounts what the researcher has found, and makes suppositions and suggestions of what it could mean without fully engaging the powerful theory to really make meanings that can, following White again, show that the theory has been tested, and is able to explain the data in ways that enable the researcher to create knowledge that adds to what we already know. These findings can then be built on by other researchers as part of the abstracted explanations they may offer for why different parts of the truth, and new solutions to current problems still need to be sought, and found. 

Theory is powerful. Or, it can be when used to actively theorise – to make meanings that are new, or additional to those we already have access to. This process is not simple, or linear. It requires immersion in your data, it requires you to suspend some of your assumptions or beliefs about what is truth, and what your data is saying, so you can allow the theory to act as a lens with which to look at your data with more ‘naïve’ or unassuming eyes. Theorising is an iterative, at times frustrating process, that is both intellectually and personally challenging. But, skimping this process to get the thesis done belies a misunderstanding of what the doctorate – or research – is. It is not (just) the thesis, or the paper. It is the process of engaging with current truths and meanings, findings under-explored problems and questions, and working to make meanings that will add to our knowledge about the world, and how we live in it (and that will transform your own understandings and knowledge).

In these uncertain, and challenging times in which we all live, we all need to embrace the difficult act of theorising – we need to reclaim the verb, over the noun. We need to embrace the research process and the learning therein – both personal and professional. The product that emerges in the form of that thesis or paper – and its author and readers – will be the richer for it. 


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.