Web Survey Bibliography
Over the last ten years the number of people using the Internet for health information and advice has grown rapidly. Many people trust the information and advice they find online although this trust may be misplaced. Indeed in a systematic meta-analysis of health website evaluations, 70% of studies concluded that quality is a problem on the Internet (Eysenbach et al., 2002). In the face of such variable quality, how do health consumers decide whether or not to trust the information and advice they find online? To address this question a review of Internet health use over the last ten years has been instigated with the intention of examining the attitudes and behaviour of online health consumers at 5 year intervals. This paper reports on the findings of that decade in e-health.
In the year 2000 a large scale questionnaire was developed to assess online trust across a number of domains including health (Briggs et al., 2002). The data from the questionnaire was used to develop a staged model of trust. This model noted that design factors and visual appeal appeared to be strong predictors of early rejection and mistrust of a health website whilst content features such as source credibility and personalisation appeared to be more predictive of trust and selection.
In 2005 the researchers sampled online health consumers again with two main objectives. Firstly, to generate an updated picture of the online health landscape, providing information on the kinds of websites people were accessing and the types of information they were seeking. Secondly, to build upon the original staged model of trust and so increase understanding of the process by which trust perceptions are translated into relevant behaviour. To these ends a revised trust questionnaire was developed reflecting the importance of both design and social identity issues. In addition questions specific to e-health (notably perceived threat, coping, and information checking and corroboration) were included. As predicted, these variables added to the ability of the model to predict variance in both trust and readiness to act upon the advice provided by the site. The results of the 2005 questionnaire data showed that women were still the predominant users of the Internet for health advice but that the sites they were seeking had changed from 2000. A key difference between the 2005 and 2000 data was the rise in use of ‘less regulated’ health sites (Sillence et al., 2006). These sites were typically run by individual ‘experts’ and marked the growing interest in Patient Experience (PEx) material online. This interest was reflected in the 2011 revised questionnaire which included items designed to measure the exposure to and importance of PEx material. The preliminary findings indicate that whilst PEx material predicts whether or not people like the site it negatively predicts their trust perceptions and has no bearing on their subsequent actions.
Collectively the findings of this 10 year study provide valuable insights into the design of trustworthy health websites, our understanding of the process between trust and behavioural outcomes and the provision of PEx websites. This should be of interest to researchers, health practitioners, providers and policy makers.
Social Science Research Network (abstract) / (full text)
Web Survey Bibliography - Research on Internet (646)
- Tablets and Smartphones and Netbooks, Oh My! Effects of Device Type on Respondent Behavior; 2013; Ross, H., Mendelson, J., Lackey, M.
- Using Web Ex to Conduct Usability Testing of an On-Line Survey Instrument; 2013; Stettler, K.
- Online Fundraising Essentials, Second Edition; 2013; Stevenson, S. C.
- Tips for Evaluating Online Effectiveness; 2013; Stevenson, S. C.
- Worldwide online research spending; 2012
- The rise of the "connected viewer"; 2012; Smith, A., Boyles, J. L.
- The integration of facebook into class management: an exploratory study; 2012; Chou, P. N.
- Selection bias of internet panel surveys: A comparison with a paper-based survey and national governmental...; 2012; Tsuboi, S. et al.
- Research company spotlight - Mobile surveys; 2012
- Redeveloping the research section of Meningitis UK's website — A case study report; 2012; Witt, J. et al.
- Guide to social science data preparation. Best practice throughout the data life cycle; 2012
- Google et Médiamétrie créent une audience bimédia; 2012; Gonzales, P.
- Ebook readings jumps, print book reading declines; 2012; Rainie, L., Duggan, M.
- Better customer in sight in real time; 2012; Macdonald, E., Wilson, H. N., Konus, H.
- Better Answers to Basic Questions: Enhancing the accuracy of online reach and audience metrics; 2012; van Dam, P. H., van Ossenbruggen, R., Voorend, R.
- WebSM Study: Survey software features overview ; 2012; Vehovar, V.; Cehovin, G.; Kavcic, L.; Lenar, J.
- Web Panels; 2012; Bethlehem, J., Biffignandi, S.
- Challenges of assessing the quality of a prerecruited probability-based panel of internet users in...; 2012; Struminskaya, B., Kaczmirek, L.
- Deep Data: Qualitative Approaches to E-Research in the Digital Age; 2012; Salmons, J.
- The use of new technologies on the British Birth Cohort Studies; 2012; Calderwood, L.
- Opportunities and Challenges for the Digital Researcher; 2012; Blank, G., Morey, Y.
- Reliable Online Social Network Data Collection; 2012; Abdesslem, F. B., Parris, I., Henderson, T.
- Little experience with technology as a cause of nonresponse in online surveys; 2012; Struminskaya, B., Schaurer, I., Kaczmirek, L., Bandilla, W.
- The Impact of Mobilization Media on Off-Line and Online Participation: Are Mobilization Effects Medium...; 2012; Vissers, S., Hooghe, M., Stolle, D., Maheo, V.-A.
- Succinct Survey Measures of Web-Use Skills; 2012; Hargittai, E., Hsieh, Y. P.
- Where gamification came from and why it could be here to stay; 2012; Ewing, T.
- Gamification 101 - from theory to practice - part II ; 2012; Puleston, J.
- The impact of two-stage highly interesting questions on completion rates and data quality in online...; 2012; M, Hansen, J. M; Smith, S. M.
- User models as revealed in web-based research services; 2012; Bodoff, D., Raban, D.
- User agent; 2011
- Unpublisihed internal Google report on break off rates by device type; 2011; Callegaro, M.
- The GfK NOP Media Efficiency Panel; 2011; Moy, C. et al.
- Online survey research: Findings, Best practices, and future research; 2011
- GRE® program announces big benefits and big savings for GRE® test takers worldwide; 2011
- Google and Kantar develop measurement panel; 2011
- Going online with assessment; 2011; Burke, E. et al.
- Exploring the digital nation. Computer and Internet use at home; 2011
- Ethical issues in Internet research; 2011; Hoerger, M., Currell, C.
- ESOMAR AND CASRO submission to the W3C tracking protection working group - Market research techniques...; 2011
- A Methodological Inference towards the Quantification of Technological Frames ; 2011; Cachia, E., Camilleri, P.
- The Battle For Business Data: New Technologies Critical To Researchers' Arsenal; 2011; Anderson, J.
- A Comparison of Internet-Based Participant Recruitment Methods: Engaging the Hidden Population of Cannabis...; 2011; Temple, E. C., Brown, R. F.
- The Perils of Online Surveys; 2011; McCullough, P. R.
- Mixed methods designs in marketing research; 2011; Harrison, R. L., Reilly, T. M.
- Development and Validation of a Web-Based Questionnaire for Surveying Skydivers; 2011; Nilsson, J.; Friden, C.; Buren, V.; Ang, B.
- Facebook sampling methods: some methodological proposals; 2011; Macrì, E., Tessitore, C.
- Survey Says? A Primer on Web-Based Survey Design and Distribution; 2011; Oppenheimer, A. J., Pannucci, C. J., Kasten, S. J., Haase, S. C.
- Improving online surveys; 2011; Puleston, J.
- Visiting item non-responses in internet survey data collection; 2011; Albaum, G., Roster, C. A., Smith, S. M., Wiley, J. B.
- Estimating nonresponse bias and mode effects in a mixed-mode survey; 2011; Lugtig, P. J., Lensvelt-Mulders, G. J., Frerichs, R., Greven, A.
