Surveys – Data Big and Small

The visualization of personal networks

I am pleased to co-organize with Vincent Lorant of UCLouvain a special session on “The visualization of personal networks” at the forthcoming INSNA Sunbelt conference (12-16 July 2022, Cairns, Australia, and online).

Personal network data collection methods allow describing the composition and the structure of an individual’s (hereafter ego) social network. This method has been implemented in different domains such as migration, drug use, mental health, aging, education, and social welfare. Over the last years, these data have also been used to provide respondents with visualizations of their personal network, using different algorithms and customizing results through computer assisted data collection. Visualization gives valuable feedback to the respondent, improves data validity and may trigger positive behavioural changes, notably in vulnerable individuals or groups. Yet, visualization is not a free lunch. Recent research has evidenced the ethical dilemmas of providing such feedback to individuals: ego’s social life is being exposed, the researcher may be exposed as well, and such feedback may imply some contractual exchanges or therapeutic implications that require attention.

This session aims to describe the stakes of different visualization approaches to personal networks with different populations. We welcome qualitative and quantitative papers addressing issues related to the implementation of visualization or reports of personal networks in terms of techniques, levels of respondent’s satisfaction with visualization, conditions under which visualization is recommended or discouraged, and effects of the personal network visualization for the respondent.

More information on the conference and the submission process is available here.

Sharing Networks 2017: pen-and-paper fieldwork in a big data world

I’m excited to report that earlier this month, I ran the second wave of data collection for our Sharing Networks research project at OuiShare Fest 2017!

*Publicizing the survey at OuiShare Fest 2017*

To understand how people form and reinforce face-to-face network ties at such an event, I fielded a questionnaire with the help of a committed and effective team of co-researchers. It is a “name generator” asking respondents to name those they knew before the OuiShare Fest, and met again there (“old frields”); and those they met during the event for the first time (“new contacts”). Participants then have to choose those among their “old” and “new” contacts, that they would like to contact again in future for joint projects or collaborations.

Interestingly, my good old pen-and-paper questionnaire still gives a lot of insight that digital data from social media cannot provide – just like a highly computer literate community such as this feels the need to meet physically in one place every year for a few days. Like trade fairs that flourish even more in the internet era, the OuiShare Fest gathers more participants at each edition. They meet in person there, which is why they are to be invited to respond in person too.

*One part of the Sharing Networks 2017 onsite survey team.*

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Sharing Networks study 2017: Moving online!

I’m so excited that earlier this month, I ran the second wave of data collection for my Sharing Networks research project at OuiShare Fest 2017!

*The Sharing Networks 2017 survey team (incomplete).*

The study aims to map the collaborative economy community that gathers at OuiShare Fest, looking at how people network and how this fosters the emergence of new trends and topics.

During the event, a small team of committed and effective co-researchers helped me interview participants. We used a questionnaire with a “name generator” format, typically used in social network analysis to elicit people’s connections and reconstitute their social environment.

Specifically, we asked respondents to name people they knew before the

*Results of our Sharing Networks 2016 survey have now become a T-shirt.*

OuiShare Fest, and met again there (“old friends”), and people they met during the Fest for the first time (“new contacts”). Then we asked them to choose, from among the “old” and “new” they had named, those they would like to contact again with soon, for example for joint projects or collaborations.

I am very happy with the result: 160 completed interviews over three half-days! But it is still not enough: participants to the Fest were much more numerous than that, and in social network analysis, it is well-known that sampling is insufficient, and one needs to get as close to exhaustiveness as possible.

Therefore, for those OuiShare Fest 2017 participants that we did not manage to interview, there is now an opportunity to complete the questionnaire online.

If you were at the Fest and we did not talk to you, please do participate now! It takes less than 8 minutes, and you will help the research team as well as the organization of the Fest.

Many thanks to the team of co-researchers who helped me, the OuiShare team members who supported us, and all respondents.

More information about the Sharing Networks study is available here.

Highlights from results of last years’ Sharing Networks survey are available here.

Networks in the collaborative economy: social ties at the OuiShare Fest 2016

The OuiShare Fest brings together representatives of the international collaborative economy community. One of its goals is to expose participants to inspiring new ideas, while also offering them an opportunity for networking and building collaborative ties.

At the 2016 OuiShare Fest, we ran a study of people’s networking. Attendees, speakers and team members were asked to complete a brief questionnaire, on paper or online.Through this questionnaire, we gained information on the relationships of 445 persons – about one-third of participants.

Ties that separate: the inheritance of past relationships

For many participants, the Fest was an opportunity to catch up with others they knew before. Of these relations, half are 12 months old at most. About 40% of them were formed at work; 15% at previous OuiShare Fests or other collaborative economy experiences; 9% can be ascribed to living in the same town or neighborhood; and 7% date back to school time.

Figure 1 is a synthesis of these “catching-up-with-old-friends” relationships, in the shape of a network where small black dots represent people and blue lines represent social ties between them. At the center of the graph are “isolates”, participants who had no pre-existing relationship among OuiShare Fest attendees. The remaining 60% have prior connections, but form part of separate clusters. Some of them (27%) form a rather large component, visible at the top of the figure, where each member is directly or indirectly connected to anyone else in that component. There are also two medium-sized clusters of connected people at the bottom. The rest consists of many tiny sub-groups, mostly of 2-3 individuals each.

Ties that bind: new acquaintances made at the event

Participants told us that they also met new persons at the Fest. Figure 2 enriches Figure 1 by adding – in red – the new connections that people made during the event. The ties formed during the Fest connect the clusters that were separate before: now, 86% of participants are in the largest network component, meaning that any one of them can reach, directly or indirectly, 86% of the others.

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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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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.

The power of survey data: Eurostat Users’ Conference

In 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:

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:

Paola Tubaro ANAMIA INSNA Sunbelt2014 St. Pete's Beach FL 22.02.2014 from Bodyspacesociety Blog

Ties that separate: the inheritance of past relationships

Ties that bind: new acquaintances made at the event

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