Wiki Surveys: Open, Adaptive, and Quantifiable Social Data Collection

18 Feb
Friday, 02/18/2011 7:30am to 9:00am

Matthew Salganik
Princeton University
Department of Sociology

Computer Science Building, Rms 150 & 151

Research about attitudes and opinions is central to socialscience and relies on two common methodological approaches: surveysand interviews. While surveys allow researchers to quantify largeamounts of information quickly and at a reasonable cost, they areroutinely criticized for being "top-down" and rigid. In contrast,interviews allow unanticipated information to "bubble up" directlyfrom respondents, but are slow, expensive, and hard to quantify. Thistension between openness and quantifiability is at the heart of the debate about quantitative and qualitative approaches to social research. Advances in computing technology now enable a hybrid approach, wiki surveys, which combines the quantifiability of a survey with the openness of an interview. We draw on principles undergirding successful information aggregation projects, such as Wikipedia and the Linux operating system, to propose several general criteria that wiki surveys should satisfy. We then present results from, a free and open source website that we created which allows groups all over the world to deploy wiki surveys. To date, over 750 wiki surveys have been created, and they have collected over 25,000 ideas and 1.5 million votes. We describe some of the methodological challenges involved in collecting and analyzing this type of data, and present case studies of wiki surveys created by the New York City Mayor's Office and the Organization for Economic Cooperation and Development (OECD). The paper concludes with a discussion of limitations and how some of these limitations might be overcome with additional research. (Joint work with Karen Levy)


Matthew Salganik is an Assistant Professor in the Department of Sociology at Princeton University. His interests include social networks, quantitative methods, and web-based social research. One main area of his research has focused on developing network-based statistical methods for studying populations most at risk for HIV/AIDS. A second main area of work has been using the World Wide Web to collect and analyze social data in innovative ways. Salganik's research has been published in journals such as Science, PNAS, Sociological Methodology, and Journal of the American Statistical Association. His papers have won the Outstanding Article Award from the Mathematical Sociology Section of the American Sociological Association and the Outstanding Statistical Application Award from the American Statistical Association. Popular accounts of his work have appeared in the New York Times, Wall Street Journal, Economist, and New Yorker. Salganik's research is funded by the National Science Foundation, National Institutes of Health, Joint United Nations Program for HIV/AIDS (UNAIDS), and Google.