Arguing from qualitative data

One of the main persistent queries I get from research students is about how to develop an argument using qualitative data. When you are sitting with a trove of diverse narratives, how do you shape these into something interesting and important without losing complexity and while letting people speak for themselves as much as you can? This is difficult, painstaking work. In the current political context, it is crucial that we take pains to develop our data into arguments which are relevant and substantive: for some of us, this will be our most useful form of activism. While I have many doubts about our ability to deploy knowledge progressively in what has been called a ‘post-truth’ era, I am not yet ready to give up on the political potential of thoughtful social research.

This post does not contain advice about data analysis but about what happens afterwards: the interpretations which can be created from data once they have been synthesised into categories or themes, once an understanding of key trends has been reached and any particularly interesting or significant cases identified. You will probably have engaged in some form of coding to get here, whether software-based or by hand. Of course, the distinction between analysis and interpretation is permeable and often arbitrary: interpretation frequently starts at the data collection stage (or in bad research, before it), when arguments begin to form in your mind. But in many projects there will come a point when it is necessary to shift up a gear intellectually. What do you really want to say about these data, and crucially, why? At this point, you could try the following:

1. Examine your motivations. Are you preoccupied with being clever and making your mark, or are you committed to saying something relevant and useful which you can evidence? Academia tends to showcase the former at the expense of the latter – indeed, research has shown that the pressure to innovate in natural sciences often leads to ‘bad science’ being published which prioritises surprising findings that are often wrong. Decide to show integrity in your work.

2. Go back to your rationale and research questions (this sounds obvious, but many students fail to do it). Why did you deem this study important, and what did you originally want to know? Of course, you are not bound by your original aims: often the process of research shifts our paradigm of inquiry because our data tell us unexpected things. We should be alert to this possibility (and remember that deriving unforeseen conclusions from rigorous analysis is different from focusing on an anomalous finding because it will help you to make a splash). Revisiting your original aims will help you focus on what your data say, whether you set out to discover it or not.

3. Go back to the literature. Whether this is your theoretical framework (if you have one) or the empirical literature review (or both), check back in with the existing field to figure out how your data speak to it. Do they merely confirm what has already been written or are there new stories, unanswered questions or anomalies which need to be explored? If you are using a particular theory, are your data consistent with it or do they expose any gaps or weaknesses? If you analyse data in enough depth you will usually find challenges to existing theoretical frameworks: when developing an argument, it is better to start here than plucking something out of thin air based on a cursory glance through your dataset. Resist the temptation to name, to speak, to conclude before you are ready. Build on the intellectual work of others – this is how understanding becomes full and deep. If you need different theories or literatures to make sense of your data, go and find them: and make a point of seeking out diverse perspectives. If your intellectual canon consists mostly of white men your analysis will be much weaker for it.

4. Analysis is often a process of shuttling between theory and data. As you make these journeys, check that you are clear about the concepts you carry, and how you are using them. Do you have a sense of what ‘power’ might look like? Have you thought about how to actually ‘do’ intersectionality in empirical research? Do not carry ‘black boxes’ – empty versions of concepts which can be inserted into an argument as conclusive, but with nothing going on inside. Agency is a good example. If you think you can identify agency in your data, shuttle back to the theoretical definition, then forward into your data to consider if you can really see it in practice. What differentiates agency from action? If you think you can identify moments of agency, what are the broader implications? (the ‘so what?’ question – see below)

5. Be honest about what your data actually support. In the context of marking criteria (and scholarly norms) which prioritise ‘originality’, students often create arguments which sound lovely but bear little relation to their dataset. Beware ‘armchair theorising’ which is not grounded in your research: this might be your pet idea, but are you sure you can evidence it? Beware buzzwords which explain nothing, merely describe the familiar in different terms, and/or are just thrown in when we don’t know what else to say. Steer clear of inventing your own terms or concepts unless you have the data to back them up – and this often takes years.

6. Know the difference between novelty and significance. The latter implies an ability to challenge received wisdom in a substantive way, and sometimes the most obvious story about your data is not the most significant one. You might interview 40 women architects: the majority might highlight pay inequity and persistent everyday sexism, but reflect favourably on initiatives designed to encourage women to apply for promotion. This is important, although nothing we don’t already know. What might be more significant is that the two black women in your sample had experienced specific forms of gendered racism which meant that initiatives around ‘promoting women’ were not particularly helpful. These cases, alongside other studies, might help to evidence the argument that equality initiatives situate white women’s successes as a proxy for women as a whole, creating the illusion of collective progress and masking the specific difficulties black women face. When arguing from your data, you might prioritise this story over the more pedestrian majority narrative we have heard many times before. This choice is a political one, and this is the value of qualitative research: it allows us to dig deeper than the majority story and explore the nuances of social issues.

7. Exploring those nuances means engaging with the ‘why’ questions about the trends, anomalies and interesting cases in your data. This also requires an understanding of issues around ‘voice’ in qualitative research and the potential pitfalls of that term. The common practice of using social research to give people a ‘voice’ is a laudable (if perhaps doomed) attempt to elevate marginalised narratives and avoid imposing ‘false consciousness’ on participants. We could talk for days about the ethics and politics of this: for now however, I want to highlight the difference between honouring people’s experiences and perspectives and taking them at face value. There is no pure ‘voice’ prior to politics. Consider the inappropriateness of taking racist ‘immigration concerns’ expressed by white working class people as given, without deconstructing the white supremacist culture in which they resent and blame people of colour for their economic woes. Consider the use of cis women’s rape trauma in advocating for trans women’s exclusion from women-only space. Engaging rigorously with qualitative data requires us to set experience in context and explore how it is produced and framed.

8. Once your argument starts coming together, ask yourself ‘so what?’ How does it shed light on broader economic, social and/or political issues or concerns? This isn’t about micro- versus macro design: often in-depth research with very small samples can illuminate wider debates with more insight than much larger studies. The ‘so what?’ test refers to your mindset when you argue from your data. Are you content to tell a nice story, or do you want to try to influence something to change? Again, your ambitions can be quite small, and it is often more practical to set your sights on something specific or local than to make claims which are too grandiose (which will take you right back to what your data can actually support).

9. As your argument takes shape, try writing an abstract of your thesis or dissertation – this will help you to construct a narrative which is focused and makes logical sense. You can also outline chapters and sub-headings using Pat Thomson’s technique for avoiding ‘blocky’ writing: this is a really useful way to get that coveted narrative flow. Keep your abstract and outline handy as you write up, so you can amend them and stay focused as your argument develops. Start to enjoy it – watching a research narrative emerge is exciting, and research does have political potential. Knowledge may not change the world, but it can be used by progressive movements in a variety of different ways. What if you were able to construct a catalogue of police brutality against sex workers in your local area? Or a detailed narrative showing how a school has negotiated racist government policies and protected refugee children in their midst? We will need all the tools we can get in the years to come: if you can furnish us with any, I personally thank you for that. 💜

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Author: alisonphipps

Academic working on issues in the politics of the body - sexual violence, sex work, reproductive justice. Feminist; queer; Prince.

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