Web Survey Bibliography
User modeling is traditionally applied to systems were users have a large degree of control over their goals, the content they view, and the manner in which they navigate through the system. These systems aim to both recommend useful goals to users and to assist them in achieving perceived goals. Systems such as online or telephone surveys are different in that users have only a singular goal of survey completion, extremely limited control over navigation, and content is restricted to prescribed set of survey tasks; changing the user modeling problem to one in which the best means of assisting users is to identify rare-actions hazardous to their singular goal, by observing their interactions with common contexts. With this goal in mind, predictive mechanisms based on a combination of Machine Learning classifiers and survey domain knowledge encapsulated in sets of rules are developed that utilize user behavioral, demographic, and survey state data in order to predict when user actions leading to irreparable harm to the user's singular goal of successful survey completion will occur. We show that despite a large class imbalance problem associated with detecting these actions and their associated users, we are able to predict such actions at a rate better than random guessing and that the application of domain knowledge via rule-sets improves performance further. We also identify traits of surveys and users that are associated with rare-action incidence. For future work, it is recommended that existence of potential sub-concepts related to users who perform these rare-actions be explored, as well as exploring alternative means of identifying such users, and that system adaptations be developed that can prevent users from performing these rare and harmful actions.
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Web survey bibliography (183)
- Using experts’ consensus (the Delphi method) to evaluate weighting techniques in web surveys not...; 2017; Toepoel, V.; Emerson, H.
- A Partially Successful Attempt to Integrate a Web-Recruited Cohort into an Address-Based Sample; 2017; Kott, P. S., Farrelly, M., Kamyab, K.
- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
- Inferences from Internet Panel Studies and Comparisons with Probability Samples; 2016; Lachan, R.; Boyle, J.; Harding, R.
- Integration of a phone-based household travel survey and a web-based student travel survey; 2016; Verreault, H.; Morency, C.
- Estimation and Adjustment of Self-Selection Bias in Volunteer Panel Web Surveys ; 2016; Niu, Ch.
- Calculating Standard Errors for Nonprobability Samples when Matching to Probability Samples ; 2016; Lee, Ad.; ZuWallack, R. S.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Evaluating Three Approaches to Statistically Adjust for Mode Effects; 2016; Kolenikov, S.; Kennedy, C.
- Linearization Variance Estimators for Mixed ‒ mode Survey Data when Response Indicators are Modeled...; 2016; Demnati, A.
- Options for Fielding and Analyzing Web Surveys; 2016; Schonlau, M.; Couper, M. P.
- Report of the Inquiry into the 2015 British general election opinion polls; 2016; Sturgis, P., Baker, N., Callegaro, M., Fisher, St., Green, J., Jennings, W., Kuha, J., Lauderdale, B...
- Solving the Nonresponse Problem With Sample Matching?; 2016
- Online and Social Media Data As an Imperfect Continuous Panel Survey; 2016; Diaz, F.; Garmon, F.; Hofman, J. K.; Kiciman, E.; Rothschild, D.
- Quota Controls in Survey Research.; 2016; Gittelman, S. H.; Thomas, R. K.; Lavrakas, P. J.; Lange, V.
- Scientific Surveys Based on Incomplete Sampling Frames and High Rates of Nonresponse; 2016; Fahimi, M.; Barlas, F. M.; Thomas, R. K.; Buttermore, N. R.
- Doing Surveys Online ; 2016; Toepoel, V.
- Using Mobile Phones for High-Frequency Data Collection; 2015; Azevedo, J. P.; Ballivian, A.; Durbin, W.
- On Bias Adjustments for Web Surveys; 2015; Fan, L.; Lou, W.; Landsman, V.
- The quality of data collected using online panels: a decade of research ; 2015; Callegaro, M.
- Does the use of mobile devices (tablets and smartphones) affect survey quality and choice behaviour...; 2015; Liebe, U., Glenk, K., Oehlmann, M., Meyerhoff, J.
- Web-based survey, calibration, and economic impact assessment of spending in nature based recreation; 2015; Paudel, K. P., Devkota, N., Gyawali, B.
- Using Web Panels for Official Statistics; 2014; Bethlehem, J.
- Self-reported cheating in web surveys on political knowledge; 2014; Jensen, C., Thomsen, J. P. F.
- Keeping Surveys Valid, Reliable, and Useful: A Tutorial; 2014; Greenberg, M. R., Weiner, M. D.
- Prioritisation of alternatives with analytical hierarchy process plus response latency and web survey...; 2014; Barone, S. Errore, A., Lombardo, A.
- A critical review of studies investigating the quality of data obtained with online panels based on...; 2014; Callegaro, M., Villar, A., Yeager, D. S., Krosnick, J. A.
- Online panel research: History, concepts, applications and a look at the future; 2014; Callegaro, M., Baker, R., Bethlehem, J., Goeritz, A., Krosnick, J. A., Lavrakas, P. J.
- Using Paradata to Predict and to Correct for Panel Attrition in a Web-based Panel Survey; 2014; Rossmann, J., Gummer, T.
- Improving cheater detection in web-based randomized response using client-side paradata; 2014; Dombrowski, K., Becker, C.
- Modelling ”don’t know” responses in rating scales; 2014; Manisera, M., Zuccolotto, P.
- User Modeling via Machine Learning and Rule-Based Reasoning to Understand and Predict Errors in Survey...; 2013; Stuart, L. C.
- Comparison of Three Modes for a Crime Victimization Survey; 2013; Laaksonen, S., Heiskanen, M.
- The Short-term Campaign Panel of the German Longitudinal Election Study 2009. Design, Implementation...; 2013; Steinbrecher, M., Rossmann, J.
- Too Fast, Too Straight, Too Weird: Post Hoc Identification of Meaningless Data in Internet ; 2013; Leiner, D. J.
- Assessing Nonresponse Bias in the Green Technologies and Practices Survey; 2013; Meekins, B., Sverchkov, M., Stang, S.
- Web Panel Representativeness; 2013; Bianchi, A., Biffignandi, S.
- On the Impact of Response Patterns on Survey Estimates from Access Panels; 2013; Enderle, T., Muennich, R., Bruch, C.
- Unit Nonresponse and Weighting Adjustments: A Critical Review; 2013; Brick, J. M.
- Adjusting for bias in a mixed-mode CAWI survey on University students ; 2013; Clerici, R., Giraldo, A.
- A probability-based web panel for the UK: What could it look like?; 2013; Nicolaas, G.
- Panel Attrition: Separating Stayers, Sleepers and Other Types of Drop-Out in an Internet Panel; 2013; Lugtig, P. J.
- Speeding and Non-Differentiation in Web Surveys: Evidence of Correlation and Strategies for Reduction...; 2013; Zhang, Che.
- Web Versus Outbound: A Mode Face-Off Following the Presidential Debate; 2013; Marlar, J.
- The Effects of Errors in Paradata on Weighting Class Adjustments: A Simulation Study; 2013; West, B. T.
- Practical tools for designing and weighting survey samples; 2013; Valliant, R. L., Daver, J. A., Kreuter, F.
- Moving an established survey online – or not?; 2013; Barber, T., Chilvers, D., Kaul, S.
- Measuring working conditions in a volunteer web survey; 2013; de Pedraza, P., Villacampa, A.
- Propensity Score Weighting – Can Personality Adjust for Selectivity?; 2013; Glantz, A., Greszki, R.
- Eurobarometer Special surveys: Special Eurobarometer 381; 2012