Web Survey Bibliography
In a CAWI (Computer-Assisted Web Interview) survey self-selection is a potential source of bias, even if it is a census survey. In fact, people who participate in the survey can be very different with respect to other eligible units who decided not to answer the questionnaire. A careful contact strategy and some weighting procedures to compensate for non-response, can be used to control bias. In this work we present the efforts made to compensate for this non-response bias in a CAWI survey conducted in early 2012 on a cohort of 8.473 first-degree course students, enrolled in academic year 2006/07 at Padova University. The survey aimed to investigate students’ attitudes, emotions and motivations towards university studies, and how they use support services. Information about university careers and students’ household characteristics are also collected (see [1]). University administrative archives provided information to contact individuals (university email, private email, phone number, mobile phone number, postal address), as well as some basic
characteristics of the students (gender, age at enrolment, type of high school, high school final grades, citizenship, place of residence, faculty of enrolment, university outcomes at the third year of course). The presence of several contact details enables us to use a mixed-mode invitation to the web survey ([2], [3], [4]). The contact to the target population was organized in four phases. In the first one we invited by email the whole cohort of student, while in the next three phases we invited by phone some individuals, chosen in order to obtain a representative sample of the entire cohort, the representativeness being considered with respect to the observed distribution of the university outcomes at the third year (information obtained from the administrative archive). This “controlled” quota sample and the mixed-mode invitation in the web survey prevent from high level of bias due to self-selection. Finally an ex-post weighting procedure based on propensity scores ([5]), performed on the basis of individual data from administrative archives, compensate for the
remaining bias.
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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