Data Theory. Simon Lindgren
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idea that anarchism in science, rather than ‘law-and-order science’, is what will help achieve progress. And, as for the risk that such an approach will lead to an unproductive situation where anything goes, we must simply trust in our own ability to think in structured ways even without following rigid rules dogmatically:
There is no need to fear that the diminished concern for law and order in science and society that characterizes an anarchism of this kind will lead to chaos. The human nervous system is too well organized for that.
(Feyerabend, 1975, p. 13)
In order to think creatively and freely in relation to existing approaches, we must allow ourselves not to think so much about which theoretical perspectives have been conventionally agreed to be compatible with one another, or about whether it is officially correct to mix certain methods together or not. In that sense, the approach that I am proposing can be metaphorically understood as a form of hacking. Because, in spite of its popular reputation to the contrary, hacking is not (only) about breaking the law through forms of electronic vandalism. As argued by cryptologist Jon Erickson (2008), hacking can in fact even be more about adhering to rules than about breaking them. The goal of hacking is to come up with ways of using, or exploiting, the structures and resources that are in operation in any given situation in ways that may be overlooked or unintended. Hacking is about applying existing tools in smart and innovative ways to solve problems. Erickson writes that:
hacked solutions follow the rules of the system, but they use those rules in counterintuitive ways. This gives hackers their edge, allowing them to solve problems in ways unimaginable for those confined to conventional thinking and methodologies.
(Erickson, 2008, p. 16)
Datafication presents us with a new data environment – with data traces, data fragments, and unsolicited data – that offers the opportunity to think in new ways about research in the ‘spirit of hacking’, aiming to surmount ‘conventional boundaries and restrictions’ for the goal of ‘better understanding the world’ (Erickson, 2008, pp. 16–18). What I describe here as anarchistic, and as hacking, may sound radical and dangerous – or maybe just plain stupid. But as a matter of fact, this approach is not very far from how science, as conceived by Bruno Latour, in general comes into being. Science and research happen in action. They are not ready made. Interest should not be focused on any alleged intrinsic qualities of approaches, but on the transformations that they undergo in their practical use. Methods do not have any ‘special qualities’, as their effects come from the many ways through which they are ‘gathered, combined, tied together, and sent back’ (Latour, 1987, p. 258). Thus, ‘we are never confronted with science, technology and society, but with a gamut of weaker and stronger associations’ (Latour, 1987, p. 259). Knowledge about society is produced through more or less messy sets of practical contingencies.
A patchwork of solutions
It is a common conviction in social research that one cannot do ‘qualitative’ and ‘quantitative’ in the same breath, as they are based on different epistemologies. But this can in fact be debated. As argued by Bryman (1984), the difference may in practice lie not so much in different philosophical views on how knowledge about social reality is achieved, but simply in the path-dependent choices that are made by individual researchers who get stuck with one paradigm or the other. While it has become an eternal truth, reiterated by researchers and methods teachers alike, that ‘the problem under investigation properly dictates the methods of investigation’ (Trow, 1957, p. 33), very few adhere to this in practice. Bryman explains that:
it is not so much a problem that determines the use of a particular technique but a prior intellectual commitment to a philosophical position. The problem is then presumably formulated within the context of these commitments. This suggestion also makes some sense in terms of the individual biographies of many social researchers, most of whom do seem to be wedded to a particular research technique or tradition. Few researchers traverse the epistemological hiatus which opens up between the research traditions.
(Bryman, 1984, p. 80)
Doing digital social research, due to the particular challenges it raises, has ‘prompted its researchers to confront, head-on, numerous questions that lurk less visibly in traditional research contexts’ (Markham and Baym, 2009, pp. vii–viii). One such issue is definitely the need to address the long-standing dispute in social science between ‘qualitative’ and ‘quantitative’ methodological approaches, which has persisted, apparently unresolvably, for more than a century – or since ancient Greece, depending on who you ask. Among researchers, there are still traces of a battle between case-oriented interpretative perspectives, on the one hand, and variable-oriented approaches focused on testing hypotheses on the other. Scholars who prefer case-oriented methods will argue that in-depth understandings of a smaller set of observations are crucial for grasping the complexities of reality, and those who prefer variable-oriented approaches will argue that only the highly systematised analysis of larger numbers of cases will allow scholars to make reliable statements about the ‘true’ order of things.
Today, however, there is an increasingly widespread consensus that the employment of combinations of ‘qualitative’ and ‘quantitative’ methods is a valid and recommended strategy, which allows researchers to benefit from their various strengths, and balance their respective weaknesses. The ‘qualitative’ tradition is seen as the more inductively oriented interpretative study of a small number of observations, while the ‘quantitative’ tradition is characterised by the deductively oriented statistical study of large numbers of cases. This has given rise to the common notion that ‘qualitative’ research produces detailed accounts through close readings of social processes, while ‘quantitative’ research renders more limited, but controlled and generalisable, information about causal relations and regularities of the social and cultural fabric.
As argued above, most researchers would agree in theory that methodological pragmatism – letting the problem to be researched, and what type of knowledge is sought, decide which method should be used – but few actually do this. This is not because researchers are liars, but because it is in fact hard to make it happen. The general direction for the work in this book, in combining the data-drivenness of interpretive (‘qualitative’) sociology, with the data-drivenness of (‘quantitative’) computational methods, most closely resembles what methodologists Norman Denzin and Yvonna Lincoln (2005, pp. 4–6) have discussed in terms of bricolage.
‘Bricolage’ is a French term, popularised by cultural anthropologist Claude Lévi-Strauss (1966), which refers to the process of improvising and putting pre-existing things together in new and adaptive ways. From that perspective, our research method is not fully chosen beforehand, but rather emerges as a patchwork of solutions, old or new, to problems faced while carrying out the research. As critical pedagogy researcher Joe Kincheloe (2005, pp. 324–5) observes: ‘We actively construct our research methods from the tools at hand rather than passively receiving the “correct”, universally applicable methodologies’, and we ‘steer clear of pre-existing guidelines and checklists developed outside the specific demands of the inquiry at hand’. So, developing your method and methodology as a bricolage means placing your specific research task at the centre of your considerations, and allowing your particular combination and application of methods to take shape in relation to the needs that characterise the given task. So this, then, is not about letting the research problem guide a choice between already existing methods. Rather, it is about re-inventing your methods in relation to each and every new challenge.
For the purpose of this book’s ambition to establish an interface between interpretive sociology and computational methods, the idea of bricolage refers to the method of piecing these two together in the shape of an emergent construction ‘that changes and takes new forms as the bricoleur adds different tools, methods, and techniques of representation and interpretation to the puzzle’ (Denzin and Lincoln, 2005, p. 4). Method must not be dogmatic, but strategic and pragmatic. I therefore argue in this book, that computational techniques, results, and visualisations can be used as elements in a new form of interpretive enterprise.
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