Web Survey Bibliography
Relevance & Research Question: Asking sensitive questions in online surveys is a difficult task as respondents might not always tell the truth and answer in a socially desirable manner. As a consequence, the prevalence of sensitive behaviors is likely to be underestimated and correlations between individual characteristics and sensitive behaviors might be biased if subgroups of the surveyed population differ in their probability to answer truthfully. The Randomized Response Technique (RRT; Warner 1965), a method proposed to face this challenge, achieved mixed results so far in providing more valid estimates than direct questioning. A related approach called the Crosswise Model (CM; Yu, Tian and Tang 2008) seems more promising. However, empirical evidence on the performance of these methods is still sparse and inconclusive, especially in the case of online surveys.
Methods & Data: In the context of an online survey on plagiarism and cheating on exams among students of two Swiss universities (N = 6494), we tested different implementations of the RRT and the CM and compared them to direct questioning using a randomized experimental design. To evaluate the different methods, we analyzed differences in prevalence estimates, breakoff rates and respondents’ impression of the techniques.
Results: Results reveal a poor performance of the RRT, which failed to elicit higher prevalence estimates than direct questioning. Using the CM, however, significantly higher prevalence estimates could be achieved.
Added Value: Our study provides a thorough experimental comparison of the RRT and the CM, which to date is missing in the literature. Furthermore, we present different implementations of the RRT and the CM, specifically tailored for use in online surveys. Implications of our findings for the future use of sensitive questions techniques such as the RRT and the CM are discussed.
GOR Homepage (abstract) / (presentation)
Web Survey Bibliography - Web surveys (3866)
- How Representative are Google Consumer Surveys?: Results From an Analysis of a Google Consumer Survey...; 2013; Krishnamurty, P., Tanenbaum, E., Stern, M. J.
- One Drink or Two: Does Quantity Depicted in an Image Affect Web Survey Responses?; 2013; Charoenruk, N., Stange, M.
- A Comparison Between Screen/Follow Item Format and Yes/No Item Format on a Multi-Mode Federal Survey; 2013; Hernandez,S. J., Arakelyan, S. N., Welch, V. E.
- Using Multiple Modes in Follow-Up Contacts in Random-Digit Dialing Surveys; 2013; Chowdhury, P. P.
- Tablets and Smartphones and Netbooks, Oh My! Effects of Device Type on Respondent Behavior; 2013; Ross, H., Mendelson, J., Lackey, M.
- Impacts of Unit Nonresponse in a Recontact Study of Youth; 2013; Mendelson, J., Viera Jr., L.
- Multi-Mode Survey Administration: Does Offering Multiple Modes at Once Depress Response Rates?; 2013; Newsome, J., Levin, K., Langetieg, P., Vigil, M., Sebastiani, M.
- Responsive Design for Web Panel Data Collection; 2013; Bianchi, A., Biffignandi, S.
- Utilizing the Web in a Multi-Mode Survey; 2013; Venkataraman, L.
- Changing to a Mixed-Mode Design: The Role of Mode in Respondents’ Decisions About Participation...; 2013; Collins, D., Mitchell, M., Toomes, M.
- Comparing the Effects of Mode Design on Response Rate, Representativeness, and Cost Per Complete in...; 2013; Tully, R.
- Internet Response for the Decennial Census – 2012 National Census Test; 2013; Reiser, C.
- The Effects of Pushing Web in a Mixed-Mode Establishment Data Collection; 2013; Ellis, C.
- Effects of Lotteries on Response Behavior in Online Panels; 2013; Goeritz, A., Luthe, S. C.
- Lotteries and study results in market research online panels; 2013; Goeritz, A., Luthe, S. C.
- Using Paradata to Study Response to Within-Survey Requests; 2013; Sakshaug, J. W.
- Paradata for Coverage Research ; 2013; Eckman, S.
- Online Fundraising Essentials, Second Edition; 2013; Stevenson, S. C.
- Tips for Evaluating Online Effectiveness; 2013; Stevenson, S. C.
- Ten questions to ask your online survey provider; 2013; Williams, D.
- Survey quality prediction system 2.0; 2013
- Practical tools for designing and weighting survey samples; 2013; Valliant, R. L., Daver, J. A., Kreuter, F.
- Paradata in web surveys; 2013; Callegaro, M.
- A nationwide web-based freight data collection; 2013; Samimi, A., Mohammadian, A., Kawamura, K.
- Mode Matters: Evaluating Response Comparability in a Mixed-Mode Survey; 2013; Bowyer, B. T., Rogowski, J. C.
- Comparing Survey Results Obtained via Mobile Devices and Computers: An Experiment With a Mobile Web...; 2013; de Bruijne, M., Wijnant, A.
- Cognitive Probes in Web Surveys: On the Effect of Different Text Box Size and Probing Exposure on Response...; 2013; Behr, D., Bandilla, W., Kaczmirek, L., Braun, M.
- Best Practice in Online Survey Research with Sensitive Topics; 2013; Kays, K., Keith, T. L., Broughal, M. T.
- Research Intentions are Nothing without Technology: Mixed-Method Web Surveys and the Coberen Wall of...; 2013; Ganassali, S., Rodriguez-Santos, C.
- Reducing Response Burden for Enterprises Combining Methods for Data Collection on the Internet; 2013; Vik, T.
- Measuring Wages Worldwide: Exploring the Potentials and Constraints of Volunteer Web Surveys; 2013; Steinmetz, S., Raess, D., Tijdens, K., de Pedraza, P.
- The Distinctiveness of Online Research: Descriptive Assemblages, Unobtrusiveness, and Novel Kinds of...; 2013; Lanfrey, D.
- Sampling, Channels, and Contact Strategies in Internet Survey; 2013; Macrì, E., Tessitore, C.
- Data Quality in PC and Mobile Web Surveys; 2013; Mavletova, A. M.
- Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds; 2013; Hasler, B. S., Tuchman, P., Friedman, D.
- Compared to a small, supervised lab experiment, a large, unsupervised web-based experiment on a previously...; 2013; Ryan, R. S., Wilde, M., Crist, S.
- From mixed-mode to multiple devices. Web surveys, smartphone surveys and apps: has the respondent gone...; 2013; Callegaro, M.
- Moving an established survey online – or not?; 2013; Barber, T., Chilvers, D., Kaul, S.
- On the Use of Latent Variable Models to Detect Differences in the Interpretation of Vague Quantifiers...; 2013; Griffin, J.
- An approach to selecting online respondents; 2013; Terhanian, G.
- By the Numbers: Theory of adaptation or survival of the fittest?; 2013; Cavallaro, K.
- Designing and conducting business surveys; 2013; Snijkers, G.,Araldsen, G., , Willimack, D. K.Jones, J.
- Modular Survey Design: A Bite Size Proposal; 2013; Kelly, F., Stevens, S., Johnson, A.
- Cyborgs vs. Monsters: Assembling Modular Surveys to Create Complete Datasets; 2013; Johnson, E. P., Siluk, L., Tarraf, S.
- Do I Have Your Full Attention?; 2013; Cape, P. J.
- Does Sample Size Still Matter?; 2013; Bakken, D. G., Bond, M.
- Optimizing Surveys for Smartphones: Maximizing Response Rates While Minimizing Bias; 2013; Lattery, K., Park Bartolone, G., Saunders, T.
- Shorter Isn't Always Better; 2013; Burdein, I.
- Mobile Research Risk: What Happens to Data Quality When Respondents Use a Mobile Device for a Survey...; 2013; Baker-Prewitt, J.
- Internet-Based Recruitment to a Depression Prevention Intervention: Lessons From the Mood Memos Study...; 2013; Morgan, A. J., Jorm, A. F., Mackinnon, A. J.
