By Cally Guerin
Researchers, and especially those working on doctorates, are advised that their work needs to be much more than a description; they must also ‘theorise’ their work. Many of us are a little unsure about what this really means, especially when instructed to ‘theorise your practice’, so here is my attempt to try and define it.
Doctoral writers generally need to tie their research to existing, well-established theories, for example, feminist theory, attachment theory, social constructivist theory. Such theories act as a lens through which the research is perceived, and often determine the direction and focus of the research.
But on another level, doctoral writers are also required to ‘theorise’ their findings. This second kind of ‘theorising’ demands that the writer step away from the mass of details to enable a big-picture view of data in order to understand its broader meanings.
Attempts to theorise can result in the production of typologies or frameworks, models or patterns, analogies or metaphors. Such high order thinking is very challenging for most of us – and can also be the most rewarding part of research. It allows for creativity in interpretation, for intuitive thinking, and even to some extent a degree of conjecture.
There are three main ways to theorise empirical results: deduction, induction and abduction. It can be helpful to think about how these processes align with research design early in the project.
Deduction works from (de = from) the general to the specific. One way to think about this is as a path that moves in the direction of rule –> case –> result. That is, begin with the general theory/rule/principle and apply it to a specific case, the context or topic of the doctoral project. The theory might say that in situation A, B will necessarily result. The researcher gathers data from the specific case and then sees whether or not the particular theory holds true. Another way of describing this ‘top down’ approach is to start with a general rule or hypothesis, examine the evidence of a particular case and reach a reliable conclusion. This approach is good for research that starts with a hypothesis that needs to be tested and causality established.
Induction works in the opposite direction, from the specific to (in = to) the general. This time we move in the direction of case –> result –> rule. This time the data shows that A leads to B which can be explained by this theory or rule. This ‘bottom up’ process starts with small details or observations, then works up through related issues to establish the general rule or explanation. Such generalising from specific events or cases thus allows prediction of likely outcomes in future or in similar situations. This approach is good for research aimed at exploring new phenomena or new perspectives on phenomena.
Abduction occurs when a probable conclusion can be taken away (ab = away) from limited information. The process here moves in the direction of result –> rule –> case. Given result B, could this rule/theory explain it? Test against case A to see if it stands. Here we start with the result observed, guess or hypothesise a theory that might explain it, then test that theory against the case. This approach can be helpful when surprising data is observed. Often this is a matter of asking why certain results have appeared, a process which sometimes requires creative and intuitive thinking. Importantly, though, the conclusions of abduction are tentative, based on the most likely explanation, so hedging language is necessary: ‘it seems probable that…’ or ‘it may be…’.
Swedberg (2012) offers the following advice when it comes to making sense of data and attempting to theorise:
What one observes is typically often covered, but not completely so, by some existing concept. In this situation it is important not to dismiss the difference, and to squeeze one’s observations into some existing category. Instead one should zoom in on the difference, magnify it, and explore if the phenomenon does not merit a new name or at least a new description or definition.
This strikes me as a wonderfully liberating way to approach the data and free up the creative and critical thinking that results in ‘theorising’.
Have you found other ways to explain the concept of ‘theorising’? Or do you understand this idea in quite different ways? Because the demand to theorise is rather vague, we welcome discussion as to how doctoral writers can make sense of it. Please share your thoughts with us.
DoctoralWriting 
Really clear and helpful. Thanks. I will recommend this to my doctoral students in sexual health. Regards. David
Glad to know this applies across all disciplines 🙂
Excellent piece. Understanding requirements of theorising is difficult but you have made it quite clear. Thank-you.
Great to hear that it’s helping!
Reblogged this on Observations of a tired sOul..
A recent (December, 2016) book by Brian Haig from the University of Canterbury, co-written with that formidably seasoned warhorse, Colin Evers of the UNSW is about abductive research. It’s very clearly written and I think it’s very useful. I asked Colin for a copy at the end of last year but he told me he only had his own copy. However, he lent me a copy so I did get to read it and based on that reading, I’d thoroughly recommend it – so much so that it will be a purchase from Sage in the near future – unless I persuade them to let me have an inspection copy…
Thank you for the recommendation, Jens – sounds like a very useful text to help think through these ideas.
Thank you very much for your sharing. It is very beneficial for us.
Thank you for the post. I am unsure it fits with my research but will help rationalise why it might not fit. I am looking at clinical outcomes in domestic violence from a clinical non theoretical position. I am not looking at the rational behind domestic violence but the clinical effects of that as defined in the DSM5.
Hi Paul – although it might not seem obvious at first, you might find that the ‘theorising’ becomes relevant when you try to make sense of the empirical data collected during your project. You’ll need to write about the meaning and implications of your findings, rather than only report the results. Is the DSM5 a type of framework? If so, that too might be part of what is meant by ‘theorising’ – perhaps you’ll be testing your data against the effects in that definition?
Thank you again. I thought about it after I posted and decided I was premature. I have looked at deduction and decided this fits well for my research. The study I am doing is unique for men but not women. So I have used the data and results from their studies into the Mental Health effects of domestic violence and applied it to males. For example. we know that repeated exposure to a range of abuse types leads to a range of mental health issues. so If this is the case for women then we will apply that to men. so if A – abuse happens regularly then we can expect that B – mental health issues will be a consequence of that. I believe the medical model would be good for this. Paul
Certainly sounds like you are getting there!
I tell students and researchers that “theory” is really just the expectations you share with your reader about your object. Or at least shareD with them before you analyzed it. In your theory section, then, you are setting your reader up for an artful disappointment. You are reminding the reader of what they expect, well aware that what you have found will challenge those expectations and therefore occasion learning.
That’s an interesting way to approach it, Thomas. Can you give an example to demonstrate how that would work? I can’t quite picture it – is it a bit like what others would label as a hypothesis?
In classical hypothesis-testing approaches the theory (in my sense) would serve as the basis for constructing the null. The hypotheses that are constructed are normally sought to be confirmed, not disappointed. That is, the “artful disappointment” comes from the rejection of the null, not the hypotheses. Theory can be used in a similar way by Bayesians to construct the “prior”. In both cases, “theory” includes the empirical results of past studies, which allows to estimate effect sizes. That is, theory is not just a set of causal laws, but also some generalized initial conditions.
But a “softer” approach to theory-as-expectation can also be taken to qualitative research. The theory provides a schema of expectations. So, by announcing that you are deploying, say, Genette’s narratology, you are fostering an expectation that the analysis will identify the order, frequency, and duration of events, and provide an account of the voice and mood of the telling. The section will be written with a presumption that the reader is familiar with Genette’s theory, and what past applications have found “works” and does not work in particular narratives. As in statistical analysis, the theory gets us to anticipate anticipate “effects” in the material to be analyzed.
Given only the theory and description of the data (in the methods section) the properly trained (“peer”) reader should be able to form a qualified opinion about what the analysis will show. That opinion should, preferably, be shown to be incomplete in the paper. Although the importance of publishing null results is becoming increasingly clear in many fields.
Thank you, Thomas. I see that you have expanded further on this in your own blog, so I’ll add the link for our readers who would like to find out more: http://blog.cbs.dk/inframethodology/.
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Thank you so much for these. They are very much helpful for my studies. Once again thanks a lot.