Reflections on the rise of transactional data in social research

1. Reflections on the rise of transactional data in social research Mike Savage Sociology & CRESC University of Manchester

2. Intellectual context These reflections arise out of • Editorship of The Sociological Review, one of the three ‘core’ UK sociology journals since 2001. • Directorship of CRESC, where I have developed an interests in how cultural institutions (DCMS, ACE, etc) actually do their research. • My research on the history of popular identies in the UK 1945-1970, (funded by a Leverhulme Major Research Fellowship). • CRESC interests in the ‘politics of method, especially of (relatively neglected) social science methods Initial arguments in Mike Savage and Roger Burrows, ‘The coming crisis of empirical sociology’ to be published in Sociology Sept 2007

3. Historical observations The two main research repertoires of social scientists, namely the national sample survey and the in-depth interview, gained precedence in the post-war years, and are now rather old. The intervening years have seen huge innovations in the generation of data and methods, yet academic social scientists don’t seem to be centrally involved in these. In the 1950s, a special effort had to be made to collect ‘social’ data, now such data is routinely produced as part of normal transactional processes, making the role of specially commissioned social research less clear. Nigel Thrift’s conception of ‘knowing capitalism’ allows us to recognise how ‘transactional data’ is both routinely produced by, and also constructs, circuits of production, distribution, exchange and consumption. The role of the academic social scientist is thus thrown into question

4. ‘Depth’ models in the social sciences In defining their identities and activities, academic social scientists often invoke ‘depth models’, implicit in positivist, realist, and hermeneutic methods • Both the interview and the sample survey are championed as a means of delving into, and revealing, ‘hidden’ social processes. • Both allow ‘inference’, ‘abstraction’ and the search for regularities, a ‘causal’ social science in which particularities are subsumed to ‘underlying’ forces • Transactional data • works through surfaces using data on whole (sub-)populations. • is concerned not with revealing the hidden, but with arraying surface data in visible and accessible form. • It concerned with particularising, as much as generalising. • Is implicated in an audit and commercial ‘neo-liberal’ climate. • Can be seen as part of ‘descriptive turn’.

5. The ‘Descriptive turn’ Recent methodologists/ theorists seek to re-instate the discredited role of the ‘descriptive’ in social research. • Historian of science John Pickstone identifies four distinct ‘ways of knowing’ (i) classificatory, (ii) analytical (iii) experimental and (iv) hermeneutic, and argues that (ii) should not be over-emphasised. • US sociologist Andrew Abbott attacks conventional ‘multi-variate’ analysis with its problematic assumptions of ‘general linear reality’ in favour of descriptive methods. • Social theorist Bruno Latour criticises the delineation of the ‘deep’ social. • Deleuze and Guattari on the ‘immanence’ of the social, with links to chaos theory, etc (cf Delanda).

6. Networks • The rise of the sample survey in the mid 20th century depended on discrediting the ‘field analysis’ approach, in which it was deemed essential to study whole populations (e.g. Tavistock Institute). • Despite the early prominence of British network research in the 1950s (Barnes, Bott, Mitchell) this tradition faded, as it is not easily amenable to study using either sample surveys or in-depth interviews. SNA now increasingly championed by physicists (Barbarasi, Watts, etc). • Transactional data allows the revival of network methods, where understanding the links between transactions, and not the attributes of the individual ‘transactor’ becomes a central research issue. • E.g. Amazon; Tesco loyalty cards; marketing research, etc

7. Visualisations • Social science depth methods have historically involved abstracting from the visual either through prioritising numbers or narratives. Some theorists (Martin Jay) talk about the ‘denigration of vision’ in the academic endeavour • The reporting of transactional data routinely deploys hybrid mixes of text, number, and the visual in ways which mutually inter-relate. • The visual, textual and numerical play off each other, and rely on a hermeneutic of accessibility and engagement • Examples include network sociograms, web pages, maps, etc

10. Mapping…. • Transactional data works through surfaces, and deploys primarily spatial and visual operators. • Speaks to the older concerns of social scientists to use graphical and mapping methods. Pierre Bourdieu’s Distinction, which uses multiple correspondence methods, and draws on field theory, offer an example of how to interpret through a mapping exercise. • Such methods can also be used to plot individuals uniquely. • Following example is from Cultural Capital and Social Exclusion (Tony Bennett, myself, Elizabeth Silva and Alan Warde).

11. Table . MCA cloud of contributing modalities, axis 1 and 2.

12. Table . MCA cloud of individuals: preferences for classical music lit up, axis 1 and 2.

13. ‘Whole’ populations? Transactional data collects data on whole populations ‘within a system’ (Amazon customers, Tesco users), rather than a random sample. This (partly) limits the applicability of this data. Increasing capacity of ‘brokers’ of to merge and link transactional data sets to allow comprehensive maps of whole populations to be conducted. The applicability of ‘data capture’ methods The neighbourhood becomes the main site around which such assemblage takes place, feeding into a new politics of classification and belonging. We need to note the limits of the sample survey with its assumptions about bounded national units.

14. Temporality • Longitudinal and panel data is increasingly central to definitions of social science methods (c.f. Abbott). • How far does the spatial focus of transactional data means that temporality is difficult to incorporate into its mapping methods? • The key arenas for the deployment of longitudinal surveys are in epidemiology and educational research. It is interesting how far this maps onto a public sector vs private sector divide. How far can transactional data be used in such research? • Issues of private sector vs public sector domains clearly relevant here…..

15. Expertise… • Bauman argues for the shift from ‘legislative’ to ‘interpretative’ intellectuals in ‘post-modern’ times. • Social scientists were historically central to the generation of specially commissioned ‘social’ knowledge but they are now not the central innovators. • Transactors (both consumers and producers) routinely create their own data, by passing the role of the social science expert (though not necessarily the finance or IT expert). • Research on transactional data challenges established canons for social science expertise through • By-passing the peer reviewed journal. • ‘Burying’ the procedures. • Co-producing the researched • Defying causality

16. Conclusions • Academic social scientists get very caught up in their own internal disputes (between quant and qual, between disciplines, etc) and have not been attentive to the rise of new methods that offer a radically different form of social research • Social scientists should not dismiss this new work as ‘un scientific’: it is highly scientific (for instance in its affiliation with the natural sciences), and speaks to important theoretical currents. • Social scientists need to critically engage with transactional research, ,e.g. by questioning its classifications, assumptions, procedures, etc • We need to reflect on ‘the politics of method’ in which academic social scientists do not enjoy a legislative position but are – at best – intermediaries between numerous agents. • A focus on ‘description’ could be a way of staging a debate between academic social scientists and work using ‘transactional data’.