Posts Tagged ‘ Surveys ’

Visualisation, mixed methods and social networks: what’s new

This morning, we had a plenary on “Visualisation and social networks in mixed-methods sociological research” at the British Sociological Association conference now going on in Manchester. This session, organized by the BSA study group on social networks that I convene with Alessio D’Angelo (BSA SNAG), builds on a special section of Sociological Research Online that we edited in 2016. Alessio and I chaired and had four top-flying speakers: Nick Crossley, Gemma Edwards (both at the University of Manchester), Bernie Hogan (Oxford Internet Institute) and Louise Ryan (University of Sheffield).

Each speaker briefly presented a case study that involved visualization, and all were great in conveying exciting albeit complex ideas in a short time span. What follows is a short summary of the main insight (as I saw it).

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The SHARING NETWORKS study

SN_box2Antonio A. Casilli and I carried out a study during the OuiShare Fest 2016, a major international get-together of the collaborative economy community that took place in Paris on 18-21 May 2016.

Our goal is to look at how people network at this important event and how their meetings, their talking to each other and their informal interactions help shape the community — so as to foster the emergence of new ideas, trends and topics.

If you were a participant, speaker, journalist organizer or teamSN_slide member/volunteer, you were asked to complete a brief questionnaire in paper format that was handed out to you upon arrival at the Fest.

If you didn’t manage to fill in the questionnaire onsite, there is still time to do so online. It takes only about 8 minutes to do so and your contribution will help scientific research as well as the organization of the Fest.

Results will be made available through the OuiShare Magazine and other online outlets.

⇒Read more here

Complete the questionnaire

Thank you for your invaluable contribution!

International Program in Survey and Data Science

A new, master’s level programme of study in Survey and Data Science is to be offered jointly by the University of Mannheim, the University of Maryland, the University of Michigan, and Westat. Applications for the first delivery are accepted until 3 January, for a start in Spring 2016. Prospective students are professionals with a first degree, at least one year of work experience, and some background in statistics or applied mathematics. All courses are delivered in English, fully online, to small classes (it’s not a MOOC!). Tuition is free, thank to support from German public funds at least for the first few cohorts.

What is most interesting about this master is its twofold core, involving both more classical survey methodology and today’s trendy data science. Fundamental changes in the nature of data, their availability, the way in which they are collected, integrated, and disseminated, have found many professionals unprepared. These changes are partly due to “big” data from the internet and digital devices becoming increasingly predominant relative to “small” data from surveys. Big data offer the benefit of fast, low-cost access to an unprecedented wealth of informational resources, but also bring challenges as these are “found” rather than “designed” data: less structured, less representative, less well documented (if at all…). In part, these changes are also due to the world of surveys changing internally, with new technical challenges (regarding for example data preservation, in a world of pre-programmed digital obsolescence), legislative issues (such as those triggered by greater awareness of privacy protection), increased demand by multiple users, and a growing need to merge surveys and data from other (such as business and administrative) sources. It is therefore necessary, as the promoters of this new study programme rightly recognize, to prepare students for the challenges of working both with designed data from surveys and with big data.

It will be interesting to see how data science, statistics, and social science / survey methodology feed into each other and support each other (or fail to do so…). There is still work to be done to develop techniques for analyzing data that allow us to gain insights more thoroughly, not just more quickly, and help us develop solid theories, rather than just uncovering new relationships that might eventually turn out to be spurious.

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The power of survey data: Eurostat Users’ Conference

survey3In the age of big data, social surveys haven’t lost their appeal and interest. Surveys are the instrument through which governments, for a long time, have gathered information on their population and economy to inform their choices. Interestingly, surveys conducted by, or for, governments are the best in terms of quality and coverage: because significant resources are invested in their design and realization, and especially because participation can be made compulsory by law (they are “official”), their sampling strategies are excellent and their response rates are extremely high. (Indeed, official government surveys are practically the only case in which the “random sampling” principles taught in theoretical statistics courses are actually applied). In short, these are the best “small data” available — and their qualities make them superior to many a (usually messy) big data collection. It is for this reason that surveys from official statistics have always been in high demand by social researchers.

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The transformative powers and the politics of data visualisation: a case with personal network data

Data visualisation is still relatively uncommon in the social sciences, and is not normally expected to be part of the standard work of a scholar (contrary, some would say, to what happens in the sciences, where visualisation is sometimes necessary to figure out the properties of objects whose existence is proven, but which cannot be seen). Yet data visualisation has an extraordinary history of accomplishments even in the social realm, as cleverly documented in a forthcoming article by James Moody and Kieran Healy; and classics such as Pierre Bourdieu valued it and attempted to use it in at least some of their work, as Baptiste Coulmont interestingly reported in a blog post.

Yet the digital age offers new opportunities for data visualisation, that are largely unexploited in the social sciences. It becomes not only a tool for the researcher — to explore data prior to conducting statistical analyses, or to present results once the work is done —  but also for the general user, the study subject, the beneficiary of any policy under discussion, and the general public. As theorists in the arts and digital humanities (but not much in the social sciences, I am afraid) have noticed, the Internet and all digital infrastructures are becoming today interfaces with databases, and users of all types are immersed in a world of data in a way that was unknown before. This means that data visualisations can have new and more transformative uses, empowering study subjects and people in general, by offering them intuitive and aesthetically appealing tools to better navigate this digital world. But it also involves new dangers, as to who sets the agenda and what aspects or characteristics of the data are being stressed; data are not just objective, ‘raw’ materials but mediated ones, and the choice of how to make them perceptible by the senses is not neutral.

At the annual conference of the British Sociological Association today in Leeds, in the Methodological Innovations Stream, I am presenting data visualisation work I have done with colleagues Antonio A. Casilli, Lise Mounier and Fred Pailler, as well as data visuliaser Quentin Bréant, as part of the research project ANAMIA. We developed three tools — one for data collection, one for data exploration and preliminary analysis, one as a basis for heuristics and presentation of results. The first was for our study subjects, the second for us researchers and our colleagues, the third for us and the larger public. My slides are available:

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Small data and big models: Sunbelt 2014

Uh, it’s been a while… I should have written more regularly! All the more so as many things have happened this month, not least the publication of our book on the End-of-Privacy hypothesis. Well, I promise, I’ll catch up!

Meanwhile, a short update from St Pete Beach, FL, where the XXXIV Sunbelt conference is just about to end. This is the annual conference of the International Network for Social Network Analysis and in the last few years, I noticed some sort of tension between the (let’s call it like that — no offense!) old-school of people using data from classical sources such as surveys and fieldwork, and big data people, usually from computer science departments and very disconnected from the core of top social network analysts, mostly from the social sciences. This year, though, this tension was much less apparent, or at least I did not find it so overwhelming. There weren’t many sessions on big data this time, but a lot of progress with the old school — which in fact is renewing its range of methods and tools very fast. No more tiny descriptives of small datasets as was the case in the early days of social network analysis, but ever more powerful statistical tools allowing statistical inference (very difficult with network data — I’ll go back to that in some future post), hypothesis testing, very advanced forms of regression and survival analysis. In this sense, a highly interesting conference indeed.  We can now do theory-building and modeling of networks at a level never experienced before, and we don’t even need big data to do so.

The keynote speech by Jeff Johnson, interestingly, was focused on the contrast between big and small data. Johnson has strong ethnographic experience with small data, including in very exotic settings such as scientific research labs at the South Pole and fisheries in Alaska. He combined social network analysis techniques, sometimes using highly sophisticated mathematical tools, with fieldwork observation to gain insight into, among other things, the emergence of informal roles in communities. His key question here was, can we bring ethnographic knowing to big data? And how can we do so?

My own presentation (apart from a one-day workshop I offered on the first day, where I taught the basis of social network analysis) took place this afternoon. I realize, and I am pleased to report, that it was in line with the small-data-but-sophisticated-modeling mood of the conference. It is a work derived from our research project Anamia, using data from an online survey of persons with eating disorders to understand how the body image disturbances that affect them are related to the structure of their social networks. The data were small, because they were collected as part of a questionnaire; but the survey technique used was advanced, and the modeling strategy is quite complex. For those who are interested in the results, our slides are here:

Network data, new and old: from informal ties to formal networks

Fig1Network data are among those that are changing fastest these days. When I say I study social networks, people almost automatically think of Facebook or Twitter –without necessarily realizing that networks have been around for, well, the whole history of humanity, long before the internet. Networks are just systems of social relationships, and as such, they can exist in any social context — the family, school, workplace, village, church, leisure club, and so forth. Social scientists started mapping and analysing networks as early as the 1930s. But people didn’t think of their social relationships as “networks” and didn’t always see themselves as “networkers” even if they did invest a lot in their relationships, were aware of them, and cared about them. The term, and the systemic configuration, were just not familiar. There was something inherently informal and implicit about social ties.

What has changed with Facebook and its homologues, is that the network metaphor has become explicit. People are nowSocial-Media-Network accustomed to talking about “networks”, and think in systemic terms, seeing their own relationships as part of a more global structure. Network ties have become formal — you have to make a clear choice and action when you add a “friend” on Facebook, or “follow” someone on Twitter; you will have a list of your friends/followers/followees (whatever the specific terminology is) and monitor changes in this list. You know who the friends of your friends are, and can keep track of how many people viewed your profile /included you in their “lists” / mentioned you in their Tweets. Now, everyone knows what networks are –so if you are a social network researcher and conduct a survey like in the old days, you won’t fear your respondents may misunderstand. In fact, you may not even need to do a survey at all –the formal nature of online ties, digitally recorded and stored, makes it possible to retrieve your network information automatically. You can just mine network tie data from Facebook, Twitter, or whatever service your target populations happen to be using.

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