Today’s CBNA seminar (Hamilton House, University of Greenwich, London) has been a great opportunity to present the datavisualization tools we’ve been developing with the ANAMIA project team. ANAMIA is a study of the networks structures and online sociability of persons with eating disorders. Funded by the French ANR, it’s been conducted since 2010 by Paola Tubaro (be sure to check out her new blog, Databigandsmall), myself and a bunch of other great social scientists. Given the specific challenges of our fieldwork, and the specific nature of our data, we decided to develop original tools to collect, analyze and convey our results to a plurality of social actors. Here are the slides of the seminar:
Despite their huge popularity within the scientific community, nowadays datavisualizations are still considered simply as tools for dissemination of results in the media. Yet, it is not only when communicating with the general public that dataviz come handy. They can be used when designing your fieldwork (can be inserted in questionnaires and facilitate interviews), when working with your colleagues (especially when they come from diverse fields like in our case: sociology, psychology, computer science, economics, philosophy…) and when familiarizing professionals and policymakers with your results.
We have developed new software tools for the visualization of personal network data, with different solutions for the three stages of our research: data collection, data analysis, and the shaping of heuristic narratives about data.
As far as data collection goes, ANAMIA EGOCENTER is a graphical version of a name generator, to be embedded in a computer-based survey to collect personal network data. It has turned out to be a user-friendly, highly effective interface for interacting and engaging with survey respondents.
While conducting analysis, ANAMIA CORPUS has enabled the aggregation, organization and ranking of responses to a questionnaire containing personal network information.
ANAMIA PERSONAL can also be used for analysis (and to an extent, to the receptor of the study’s result) and offers a single format to represent individual network data consistently, to compare and contrast them. It identifies important alters and ties along relevant dimensions, comparable across respondents. It is a way to ‘tell stories with data’ and to facilitate the construction of scientific explanations of social phenomena while remaining consistent with the empirical world it represents.