Web Survey Bibliography
Abstract: Cloud computing represents a new way to deploy computing technology, where dynamically scalable and virtualized resources are provided as a service over the Internet. Amazon Elastic Cloud (EC2) is an example of Infrastructure-as-a-Service that anyone can use today to access infinite computing capacity on demand. This new environment enables collaboration, resources sharing and provides the tools for traceable and reproducible computational research. This model of allocating processing power holds the promise of a revolution in scientific and statistical computing.
Bringing this new era for research and education still requires new software that bridges the gap between the scientist’s everyday tools and the cloud. For instance, making R available as a service in the cloud and allowing its use without any memory or computing constraints would benefit the broad population of statisticians and research professionals. This is what Elastic–R (www.elasticr.net) delivers. It provides a Google docs-like portal and workbench for data analysis that makes using R on the cloud even simpler than using it locally. It enables scientists, educators and students to allocate cloud resources seamlessly work with R engines and use their full capabilities from within any standard web browser.
Features include real time collaboration, sharing and re-using virtual machines, sessions, data, functions, spreadsheets, dashboards, and automatically generated macro enabled Word documents and Excel workbooks which can be synchronized in real-time with R engines on the cloud. Computationally intensive algorithms can easily be run on any number of virtual machines that are controlled from within a standard R session. Elastic-R is also an applications platform that allows anyone to assemble statistical methods and data with interactive user interfaces for the end user. These interfaces and dashboards are created visually, and are automatically published and delivered as simple web applications.
In financial environments, this allows analysts to share common data sources and dashboards and to mirror them in a familiar office environment. In an industrial environment, it allows sharing data and analyses among different production and research sites which may not have the same computing environment. Finally, since the proposed computing architecture uses a cloud as a work horse, large scale and resource demanding calculations can be carried out at a on-demand basis without the need of installing high performance computing systems locally.
Conference Homepage (abstract)
Web survey bibliography - 2010 (251)
- Exploring the digital nation. Home broadband internet adoption in the United States; 2010
- Ethical principles of psychologists and code of conduct; 2010
- Envisioning the 2020 Census; 2010; Brown, L. D., Cohen, M. L., Cork, D. L., Citro, C. F.
- Ekos' observation of MRIA study - Canadian online panels: similar or different?; 2010
- Demographics of mechanical Turk. Ceder working papers; 2010; Ipeirotis, P. G.
- Drag & Drop. A flexible method for moving objects, implementing rankings, and a wide range of other...; 2010; Neubarth, W.
- Disclosure standards; 2010
- Digital Nation: 21st century America’s progress toward universal broadband Internet access; 2010
- Movie Mobile Polls: Does Survey Mode Make a Difference?; 2010; Williams, Do.
- The Future of Internet Research; 2010; Lavrakas, P. J.
- Accounting for the effects of data collection modes in population surveys; 2010; Huang, Y. C., Thompson, M. E., Boudreau, C., Fong, G. T.
- Using administrative data to find the best medium: Examples of mixed sources and mixed modes; 2010; Hartkamp, J., Rutjes, H.
- Broadband adoption and use in America; 2010; Horrigan, J.
- Applied survey data analysis; 2010; Heeringa, S. G., West, B. T., Berglund, P.
- Applied missing data analysis; 2010; Enders, C. K.
- Application of a check-all-that-apply question for the evaluation of strawberry cultivars from a breeding...; 2010; Lado, J., Vicente, E., Manzzioni, A., Ares, G.
- A framework for understanding and applying ethical principles in network and security research; 2010; Kenneally, E., Bailey, M., Maughan, D.
- Organizational Survey of Workplace Climate: Differences in Representation Across Response Modes; 2010; Mohr, D., Osatuke, K., Moore, S., Yanovsky, B., Brassell, T., Nagy, M.
- Strategies for High Response Rates Among Hard-to-Reach Respondents: A Case Study From the Communities...; 2010; Fox, L., Mulvey, C., Yamaguchi, R., Levin, M.
- Innovative mobile research in developing countries; 2010; Bellity, E.
- Mobile location based research: Cross cultural examination of coffee culture; 2010; Morden, M., Ferneyhough, C., Grenville, A.
- Online research….and all that Jazz!; 2010; Gittelman, S. H., Trimarchi, E.
- Why are we trying to create new communities for market research purposes?; 2010; Pearson, C., Kateley, V.
- Maximizing online respondent engagement through a game-way research design; 2010; Swahar, G., Swahar, J.
- Designing questions for mixed mode data collection: What have we learnt so far?; 2010; Nicolaas, G., Campanelli, P.
- Online panel survey, Change and stability of political attitudes; 2010
- The Internet, Electoral Politics and Citizen Participation in Global Perspective; 2010; Gibson, R., Cantijoch, M.
- Internet-Based Measurement With Visual Analogue Scales: An Experimental Investigation; 2010; Funke, F.
- Continuity and Innovation in the Design of Understanding Society: the UK Household Longitudinal Study...; 2010; Laurie, H.
- Weighting Strategy for Understanding Society; 2010; Lynn, P., Kaminska, O.
- Globalpark Annual Market Research Software Survey 2009; 2010; Macer, T., Wilson, S.
- Understanding Society Innovation Panel Wave 2: Results from Methodological Experiments ; 2010; Burton, J., Laurie, H., Uhrig, S. C. N.
- Offering a Web Option in a Mail Survey of Young Adults: Impact on Survey Quality; 2010; Turner, S., Viera Jr., L., Marsh, S. M.
- Using Web-Hosted Surveys to Obtain Responses from Extension Clients: A Cautionary Tale.; 2010; Israel, G. D.
- Mobile Experience Sampling: Reaching the Parts of Facebook Other Methods Cannot Reach; 2010; Abdesslem, F. B., Parris, I., Henderson, T.
- Investigating Data Quality in Cell Phone Surveying; 2010; Lavrakas, P. J., Tompson, T., Benford, R.
- Walking in Facebook: A Case Study of Unbiased Sampling of OSNs; 2010; Gjoka, M., Kurant, M., Butts, C. T., Markopoulou, A.
- Social Networking Sites: Evaluating and Investigating their use in Academic Research; 2010; Redmond, F.
- Update on the ARF’s Quality Enhancement Process (QeP); 2010; Pettit, R.
- Elastic-R, a Google docs-like portal for data analysis in the Cloud ; 2010; Chine, K.
- Restructuring and innovations on the survey “capacity of collective tourist accommodation”...; 2010; Santoro, M. T., Staffieri, S.
- Managing the knowledge base - the DUVA system, from data entry to output tools; 2010; Then, R., Bangert, D.
- An Analyze of the Zero Price Effect on Online Business Performance - An Research Based on the Mobile...; 2010; Liu, Y., Yuan, P.
- Is there a future for “real” qualitative market research interviewing in the digital age...; 2010; McPhee, N.
- From clipboards to online research communities; 2010; Poynter, R., Cierpicki, S., Lorch, J., Zuo, B., Davis, C., Eddy, C.
- 3 screen measurement: Soccer World Cup 2010; 2010; Conry, S., Benezra, K., Singh, S.
- Dealing with Nonresponse in Survey Sampling: an Item Response Modeling Approach; 2010; Matei, A.
- Power, sample size, and optimal designs in social research; 2010; Moerbeek, M., van Breukelen, G. J. P.
- Codebook and explanatory note on the WageIndicator dataset ; 2010; Tijdens, K., van Zijl, S., Hughie-Williams, M., van Klaveren, M., Steinmetz, S.
- Modeling non-sampling errors and participation in Web surveys; 2010; Biffignandi, S.