Web Survey Bibliography
Many surveys today are affected by high nonresponse. This can be a serious problem to survey quality since nonresponse causes systematic error (bias) in the survey estimates. Given the decreasing trend in response rates and the corresponding increasing resources needed to achieve preset response rates, taking measures only at the estimation stage is no more sufficient to overcome this problem, nor efficient. Measures need to be taken also at the data collection stage. In this direction, different forms of responsive design have recently been proposed. The general objectives of responsive design have been formulated in Groves and Heeringa (2006). The main idea underlying this method is to intervene in the data collection process, in order to achieve an ultimate set of responding units that is “better balanced” or “more representative” than if no special effort is made. Interventions are settled by evaluating the sample properly as the data collection unfolds. To this purpose different indicators have been proposed, such as the balance and representativity indicators of the set of respondents and the distance between respondents and nonrespondents (Särndal, 2011, Schouten et al., 2009, Schouten et al., 2011, and Lundquist and
Särndal, 2012). These indicators are computable from selected auxiliary variables, which are known for the responding units as well as for the non-responding ones. By monitoring the indicators during the data collection process, it is possible to modify the original design during the course of the data collection, in order to obtain a better balanced ultimate response set. The recent existing literature presents many progresses in the development of this methodology. However, further investigations are needed in order to apply it in practice, also to different contexts. The aim of this paper is to evaluate the potentials of responsive design in the framework of mixed mode panels, where one mode is Web. The empirical application uses data from the on-going probability-based PAADEL panel. The PAADEL-Producer panel is an Italian regional panel of businesses in the agro-food sector managed at the CASI centre of Bergamo University. The recruitment of the panel was conducted in 2012 and lasted approximately three months. The first step recruitment was based on phone mode (maximum number of contact attempts five); the second step recruitment was based on the mixed mode approach (Web, phone, mail, fax). Using the
database of data collection of this research, first the progression of the estimates of a few variables is studied as the data collection unfolds. Next the balance and representativity of the panel are investigated at different steps of the recruitment. Finally, a set of experimental responsive designs based on alternative interventions in the data collection is artificially reproduced. Results are analyzed in a comparative way to evaluate the impact of this approach on the final estimates. Special attention is devoted to the bias reduction issue. Some thoughts on the consequences on the variability of the estimates are also proposed. The results obtained are promising. By way of example, Table 1 shows some of the results that will be described in the study.
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Web survey bibliography - 2013 (465)
- The role of gamification in better accessing reality and hence increasing data validity ; 2015; Bailey, P.; Kernohan, H.; Pritchard, G.
- Rewarding the Truth; 2015; Puleston, J.
- Tailored fieldwork design to increase representative household survey response: an experiment in the...; 2015; Luiten, A.; Schouten, B.
- Challenges with Online Research for Couples and Families: Evaluating Nonrespondents and the Differential...; 2015; Busby, D. M.; Yoshida, Ke.
- Do Incentives Commoditize Surveys Or Reinforce The Relationship Economy?; 2014; Murphy, L.
- Is it what you say, or how you say It? An experimental analysis of the effects of invitation wording...; 2014; Fazekas, Z., Wall, M. T., Krouwel, A.
- Asking Sensitive Questions: An Evaluation of the Randomized Response Technique Versus Direct Questioning...; 2013; Wolter, F.; Preisendoerfer, P.
- Developing an Inclusive Web Survey Design for Respondents with Disabilities; 2013; Jagger, J.; Schaad, A.; Davis, As.; Falcone, A. E.
- The Impact of Survey Communications on Response Rates and Response Quality; 2013; Barlas, F. M.; Falcone, A. E.; Bellamy, N. D.; Mack, A. R.
- The Smartphone Way to Collect Survey Data; 2013; Stapleton, C.
- A Glimpse Inside the Mind of a Respondent: Using Paradata to Improve Online Surveys; 2013; Pape, T.; Barron, S.
- Respondent Choice of Survey Mode; 2013; Fuchs, M.
- Mobile-Mostly Internet Users and Noncoverage in Traditional Web Surveys ; 2013; Antoun, C.; Couper, M. P.
- Pret met panels [Fun online]; 2013; Roberts, A., de Leeuw, E. D., Hox, J., Klausch, L. T., de Jongh, A.
- Leuker kunnen wij het wel maken. Online vragenlijst design: standaard matrix of scrollmatrix (We can...; 2013; Roberts, A., de Leeuw, E. D., Hox, J., Klausch, L. T., de Jongh, A.
- Development and validation of a single- item scale for the relative assessment of physical attractiveness...; 2013; Lutz, J.; Kemper, C. J.; Beierlein, C.; etc.
- Accounting for the Effects of Data Collection Method Application to the International Tobacco Control...; 2013; Thompson, M. E.; Huang, Y. C.; Boudreau, C.; Fong, G. T.; van den Putte, B.; Nagelhout, G. E.; Willemsen...
- A dual-frame sampling methodology to address landline replacement in tobacco control research..; 2013; McMillen, R. C.; Winickoff, J. P.; Wilson, K.; Tanski, S.; Klein, J. D.
- User Modeling via Machine Learning and Rule-Based Reasoning to Understand and Predict Errors in Survey...; 2013; Stuart, L. C.
- Measuring Mobile Phone Use: Self-Report Versus Log Data; 2013; Boase, J., Ling, R.
- How Sliders Bias Survey Data; 2013; Sellers, R.
- Does the first impression count? Examining the effect of the welcome screen design on the response rate...; 2013; Haer, R., Meidert, N.
- Survey Research Response Rates: Internet Technology vs. Snail Mail ; 2013; Lanier, P. A., Tanner, J. R., Totaro, M. W., Gradnigo, G.
- The impact of New Zealand's 2008 prohibition of piperazine-based party pills on young people'...; 2013; Sheridan, J., Dong, C. Y., Butler, R., Barnes, J.
- PRM144 – An adaptable methodology for the design, implementation and conduct of a web-based survey...; 2013; Yeomans, K., Kawata, A. K., Bassel, M., Burk, C. T., Daniels, S. R., Wilcox, T. K.
- The relationships among nurses' job characteristics and attitudes toward web-based continuing learning...; 2013; Chiu, Y.-L., Tsai, C.-C., Fan Chiang, C.-Y.
- Surveillance of patients post-endovascular abdominal aortic aneurysm repair (EVAR). A web-based survey...; 2013; Patel, A., Edwards, R., Chandramohan, S.
- How well do volunteer web panel surveys measure sensitive behaviours in the general population, and...; 2013; Erens, B., Burkill, S., Copas, A., Couper, M. P., Conrad, F.
- Tailoring mode of data collection in longitudinal studies; 2013; Kaminska, O., Lynn, P.
- Comparison of Three Modes for a Crime Victimization Survey; 2013; Laaksonen, S., Heiskanen, M.
- Community Life Survey: Summary of web experiment findings; 2013
- Does Stress Increase the Risk of Atopic Dermatitis in Adolescents? Results of the Korea Youth Risk Behavior...; 2013; Kwon, J. A., Lee, M., Park, E.-C., Park, S., Yoo, K.-B.
- The Short-term Campaign Panel of the German Longitudinal Election Study 2009. Design, Implementation...; 2013; Steinbrecher, M., Rossmann, J.
- Understanding Society Innovation Panel Wave 5: results from methodological experiments; 2013; Auspurg, K., Burton, J., Cullinane, C., Delavande, A., Fumagalli, L., Iacovou, M., Jaeckle, A., Kaminska...
- Bringing usability to pretesting of Business Survey Web Forms in Statistics Finland; 2013; Rouhunkoski, J.
- How do we Know Cognitive Interviewing is Any Good?; 2013; Willis, G. B.
- Survey optimisation considerations for Android, Apple and Windows 8 mobile devices; 2013; Owen, R.
- Speeding in Web Surveys: The tendency to answer very fast and its association with straightlining; 2013; Conrad, F. G.; Zhang, Che.
- About the Institute of Public Health - Data aspect; 2013; Zaletel, M.
- Analyzing Paradata to Investigate Measurement Error; 2013; Yan, T., Olson, K.
- Too Fast, Too Straight, Too Weird: Post Hoc Identification of Meaningless Data in Internet ; 2013; Leiner, D. J.
- Can timestamp analyses show the bottlenecks in web surveys?; 2013; Andreadis, I.
- Timing in a web based survey: an influential factor of the response rate; 2013; Paraschiv, D.-C.
- Achieving Synergy Across Survey Modes: Mail Contact and Web Responses from Address-Based Samples; 2013; Dillman, D. A.
- The Future of Social Media, Sociality, and Survey Research; 2013; Hill, C., Dever, J. A.
- Collecting Diary Data on Twitter; 2013; Richards, A., Dean, E., Cook, S.
- Second Life as a Survey Lab: Exploring the Randomized Response Technique in a Virtual Setting; 2013; Richards, A., Dean, E.
- Virtual Cognitive Interviewing Using Skype and Second Life; 2013; Dean, E., Head, B., Swicegood, J. E.
- Sentiment Analysis: Providing Categorical Insight into Unstructured Textual Data; 2013; Haney, C.
- Social Media, Sociality, and Survey Research; 2013; Hill, C., Dean, E., Murphy, J.