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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.

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