Web Survey Bibliography
(a) Relevance & Research Question: The proposed paper builds on findings presented by the authors at the GOR 10. High drop-out rates are considered a major shortcoming of web surveys and considerably threaten data quality. However, despite growing scholarly attention the knowledge on survey drop-out is still fractional. Previous research mainly addresses the impact of survey design, question wording, and characteristics of the respondents on survey drop-out via ex-post statistical methods. The research presented here is innovative in that the respondents are asked directly about the reasons for dropping out, the interview situation, and psychological predispositions in a follow-up survey.
(b) Methods & Data: Based on our previous research regarding survey drop-out, the principal investigators of the GLES granted funding for a series of short follow-up surveys of drops-outs. These surveys will be conducted subsequently to three consecutive online trackings of the GLES, beginning in December 2010. According to experience, a gross sample size of about 400 drop-outs per survey can be expected. Given an estimated response rate of 60 percent a net sample size of 210 to 240 per tracking is anticipated, thus providing a unique database of more than 600 interviews with drop-outs. Since the most essential items are also included in the tracking surveys, the design allows for comparisons between drop-outs and complete responders. Due to the explorative character of the research, the presentation will mainly focus on descriptive statistics as well as multivariate models illustrating our major findings.
(c) Results: First results will be available by mid-January 2011.
(d) Added Value: Follow-up surveys of respondents who dropped-out allow for an enhanced understanding of the complex processes underlying the phenomenon, especially with respect to the subjective reasons of the respondents as well as the situational influences and psychological predispositions, which cannot be studied applying ex-post statistical procedures. In this regard, our research will add to the knowledge on the reasons for drop-out in web surveys and amend both the theoretical explanations of and the prospects for reducing drop-outs.
Conference Homepage (abstract) / (presentation)
Web Survey Bibliography (6390)
- The equivalence of Internet versus paper-based surveys in IT/IS adoption research in collectivistic...; 2013; Fang, J., Wen, C., Prybutok, V.
- Examining the Gender Effects of Different Incentive Amounts in a Web Survey; 2013; Boulianne, S. J.
- Online Survey Software; 2013; Baker, J. D.
- How Do Lotteries and Study Results Influence Response Behavior in Online Panels?; 2013; Goeritz, A., Luthe, S. C.
- Mode Effects in Free-list Elicitation: Comparing Oral, Written, and Web-based Data Collection; 2013; Gravlee, C. C., Bernard, H. R., R., Jacobsohn, A., R.Maxwell, C. R.
- Incentives for college student participation in web-based substance use surveys; 2013; Patrick, M. E., Singer, E., Boyd, C. J., Cranford, J. A., McCabe, S. E.
- The effect of short formative diagnostic web quizzes with minimal feedback; 2013; Baelter, O., Enstroem, E., Klingenberg, B.
- Increasing Web Survey Response Rates in Innovation Research: An Experimental Study of Static and Dynamic...; 2013; Sauermann, H.; Roach, M.
- Sample composition discrepancies in different stages of a probability-based online panel; 2013; Bosnjak, M., Haas, I., Galesic, M., Kaczmirek, L., Bandilla, W., Couper, M. P.
- Survey of Cloud Computing; 2013; Furht, B.
- A comparison of data quality and practicality of online versus postal questionnaires in a sample of...; 2013; King, M. T., Butow, P., Olver, I., Smith, A. B.
- Up Means Good: The Impact of Screen Position on Evaluative Ratings in Web Surveys.; 2013; Tourangeau, R., Conrad, F. G., Couper, M. P.
- WebSM Study: Overview of Features of Software Packages: SurveyMonkey, QuestionPro, FluidSurveys, Wufoo...; 2012; Cehovin, G.; Vehovar, V.
- WebSM Study: Speed and efficiency of online survey tools; 2012; Cehovin, G.; Vehovar, V.
- Worldwide online research spending; 2012
- What we can learn from unintentional mobile respondents; 2012; Peterson, G.
- Using paradata to explore item-level response times in surveys; 2012; Couper, M. P., Kreuter, F.
- Using multivariate statistics, 6th Edition; 2012; Tabachnick, B. G., Fidell, L. S.
- Unintentional mobile respondents; 2012; Peterson, G.
- Tracking preference expression (DNT); 2012
- The smartphone psychology manifesto; 2012; Miller, G.
- The rise of the "connected viewer"; 2012; Smith, A., Boyles, J. L.
- The practice of social research; 2012; Babbie, E. R.
- The integration of facebook into class management: an exploratory study; 2012; Chou, P. N.
- The effects of item saliency and question design on measurement error in a self-administered survey; 2012; Stern, M. J., Smyth, J. D., Mendez, J.
- The cross platform report. Q2 -2012 - US; 2012
- Speed (necessarily) doesn’t kill: A new way to detect survey satisficing; 2012; Garland, P. et al.
- Smartphone ownership update: September 2012; 2012; Rainie, L.
- Sensitive topics in PC Web and mobile web surveys: Is there a difference?; 2012; Mavletova, A. M., Couper, M. P.
- Selection bias of internet panel surveys: A comparison with a paper-based survey and national governmental...; 2012; Tsuboi, S. et al.
- Screenwise panel: Frequently Asked Questions; 2012
- Research company spotlight - Mobile surveys; 2012
- Redeveloping the research section of Meningitis UK's website — A case study report; 2012; Witt, J. et al.
- Quality in market research. From theory to practice. 2nd Edition; 2012; Harding, D., Jackson, P.
- Participation of mobile users in traditional online studies; 2012; Jue, A.
- Online survey statistics for the mobile future. Updated with Q3 2012 data; 2012
- Ofcom technology tracker Wave 3; 2012
- Ofcom technology tracker Wave 2; 2012
- Not just playing around; 2012; Ewing, T.
- Norme di qualita' Assirm (Assirm quality rules]; 2012
- NBCU enlists Google, ComScore to track multiscreen Olympics viewing; 2012; Spangler, T.
- MRS Guidelines for online reseach; 2012
- More dirty little secrets of online panel research.; 2012
- Mobile usability; 2012; Nielsen, J., Budiu, R.
- Mobile email opens report 2nd half 2011; 2012
- Metering mobile usage. Insights from global Arbitron mobile trends panel; 2012; Verkasalo, H.
- Media tracker; 2012
- Measuring the quality of governmental websites in a controlled versus an online setting with the ‘...; 2012; Elling, S. et al.
- Measuring modern media consumption; 2012; Arini, N.
- ISO 20252. Market, opinion and social research-Vocabulary and service requirements, 2nd Edition; 2012