Web Survey Bibliography
Background: Comparison of rates of newly diagnosed HIV infections among MSM across countries is challenging for a variety of reasons, including the unknown size of MSM populations. In this paper we propose a method of triangulating surveillance data with data collected in a pan-European MSM Internet Survey (EMIS) to estimate the sizes of the national MSM populations and the rates at which HIV is being diagnosed amongst them by calculating survey-surveillance discrepancies (SSD) as a measure of selection biases of survey participants.
Methods: In 2010, the first EMIS collected self-reported data on HIV diagnoses among more than 180,000 MSM in 38 countries of Europe. These data were compared with data from national HIV surveillance systems to explore possible sampling and reporting biases in the two approaches. The Survey-Surveillance Discrepancy (SSD) represents the ratio of survey members diagnosed in 2009 (HIVsvy) to total survey members (Nsvy), divided by the ratio of surveillance reports of diagnoses in 2009 (HIVpop) to the estimated total MSM population (Npop). As differences in household internet access may be a key component of survey selection biases, we analysed the relationship between household internet access and SSD in countries conducting consecutive MSM internet surveys at different time points with increasing levels of internet access. The empirically defined SSD was used to calculate the respective MSM population sizes (Npop), using the formula Npop = HIVpop*Nsvy*SSD/HIVsvy.
Results: Survey-surveillance discrepancies for consecutive MSM internet surveys between 2003 and 2010 with different levels of household internet access were best described by a potential equation, with high SSD at low internet access, declining to a level around 2 with broad access. The lowest SSD was calculated for the Netherlands with 1.8, the highest for Moldova with 9.0. Taking the best available estimate for surveillance reports of HIV diagnoses among MSM in 2009 (HIVpop), the relative MSM population sizes were between 0.03% and 5.6% of the adult male population aged 15–64. The correlation between recently diagnosed (2009) HIV in EMIS participants and HIV diagnosed among MSM in 2009 as reported in the national surveillance systems was very high (R2 = 0.88) when using the calculated MSM population size.
Conclusions: Npop and HIVpop were unreliably low for several countries. We discuss and identify possible measurement errors for countries with calculated MSM population sizes above 3% and below 1% of the adult male population. In most cases the number of new HIV diagnoses in MSM in the surveillance system appears too low. In some cases, measurement errors may be due to small EMIS sample sizes. It must be assumed that the SSD is modified by country-specific factors.
Comparison of community-based survey data with surveillance data suggests only minor sampling biases in the former that – except for a few countries - do not seriously distort inter-country comparability, despite large variations in participation rates across countries. Internet surveys are useful complements to national surveillance systems, highlighting deficiencies and allowing estimates of the range of newly diagnosed infections among MSM in countries where surveillance systems fail to accurately provide such data.
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Web survey bibliography - 2013 (465)
- The role of gamification in better accessing reality and hence increasing data validity ; 2015; Bailey, P.; Kernohan, H.; Pritchard, G.
- Rewarding the Truth; 2015; Puleston, J.
- Tailored fieldwork design to increase representative household survey response: an experiment in the...; 2015; Luiten, A.; Schouten, B.
- Challenges with Online Research for Couples and Families: Evaluating Nonrespondents and the Differential...; 2015; Busby, D. M.; Yoshida, Ke.
- Do Incentives Commoditize Surveys Or Reinforce The Relationship Economy?; 2014; Murphy, L.
- Is it what you say, or how you say It? An experimental analysis of the effects of invitation wording...; 2014; Fazekas, Z., Wall, M. T., Krouwel, A.
- Asking Sensitive Questions: An Evaluation of the Randomized Response Technique Versus Direct Questioning...; 2013; Wolter, F.; Preisendoerfer, P.
- Developing an Inclusive Web Survey Design for Respondents with Disabilities; 2013; Jagger, J.; Schaad, A.; Davis, As.; Falcone, A. E.
- The Impact of Survey Communications on Response Rates and Response Quality; 2013; Barlas, F. M.; Falcone, A. E.; Bellamy, N. D.; Mack, A. R.
- The Smartphone Way to Collect Survey Data; 2013; Stapleton, C.
- A Glimpse Inside the Mind of a Respondent: Using Paradata to Improve Online Surveys; 2013; Pape, T.; Barron, S.
- Respondent Choice of Survey Mode; 2013; Fuchs, M.
- Mobile-Mostly Internet Users and Noncoverage in Traditional Web Surveys ; 2013; Antoun, C.; Couper, M. P.
- Pret met panels [Fun online]; 2013; Roberts, A., de Leeuw, E. D., Hox, J., Klausch, L. T., de Jongh, A.
- Leuker kunnen wij het wel maken. Online vragenlijst design: standaard matrix of scrollmatrix (We can...; 2013; Roberts, A., de Leeuw, E. D., Hox, J., Klausch, L. T., de Jongh, A.
- Development and validation of a single- item scale for the relative assessment of physical attractiveness...; 2013; Lutz, J.; Kemper, C. J.; Beierlein, C.; etc.
- Accounting for the Effects of Data Collection Method Application to the International Tobacco Control...; 2013; Thompson, M. E.; Huang, Y. C.; Boudreau, C.; Fong, G. T.; van den Putte, B.; Nagelhout, G. E.; Willemsen...
- A dual-frame sampling methodology to address landline replacement in tobacco control research..; 2013; McMillen, R. C.; Winickoff, J. P.; Wilson, K.; Tanski, S.; Klein, J. D.
- User Modeling via Machine Learning and Rule-Based Reasoning to Understand and Predict Errors in Survey...; 2013; Stuart, L. C.
- Measuring Mobile Phone Use: Self-Report Versus Log Data; 2013; Boase, J., Ling, R.
- How Sliders Bias Survey Data; 2013; Sellers, R.
- Does the first impression count? Examining the effect of the welcome screen design on the response rate...; 2013; Haer, R., Meidert, N.
- Survey Research Response Rates: Internet Technology vs. Snail Mail ; 2013; Lanier, P. A., Tanner, J. R., Totaro, M. W., Gradnigo, G.
- The impact of New Zealand's 2008 prohibition of piperazine-based party pills on young people'...; 2013; Sheridan, J., Dong, C. Y., Butler, R., Barnes, J.
- PRM144 – An adaptable methodology for the design, implementation and conduct of a web-based survey...; 2013; Yeomans, K., Kawata, A. K., Bassel, M., Burk, C. T., Daniels, S. R., Wilcox, T. K.
- The relationships among nurses' job characteristics and attitudes toward web-based continuing learning...; 2013; Chiu, Y.-L., Tsai, C.-C., Fan Chiang, C.-Y.
- Surveillance of patients post-endovascular abdominal aortic aneurysm repair (EVAR). A web-based survey...; 2013; Patel, A., Edwards, R., Chandramohan, S.
- How well do volunteer web panel surveys measure sensitive behaviours in the general population, and...; 2013; Erens, B., Burkill, S., Copas, A., Couper, M. P., Conrad, F.
- Tailoring mode of data collection in longitudinal studies; 2013; Kaminska, O., Lynn, P.
- Comparison of Three Modes for a Crime Victimization Survey; 2013; Laaksonen, S., Heiskanen, M.
- Community Life Survey: Summary of web experiment findings; 2013
- Does Stress Increase the Risk of Atopic Dermatitis in Adolescents? Results of the Korea Youth Risk Behavior...; 2013; Kwon, J. A., Lee, M., Park, E.-C., Park, S., Yoo, K.-B.
- The Short-term Campaign Panel of the German Longitudinal Election Study 2009. Design, Implementation...; 2013; Steinbrecher, M., Rossmann, J.
- Understanding Society Innovation Panel Wave 5: results from methodological experiments; 2013; Auspurg, K., Burton, J., Cullinane, C., Delavande, A., Fumagalli, L., Iacovou, M., Jaeckle, A., Kaminska...
- Bringing usability to pretesting of Business Survey Web Forms in Statistics Finland; 2013; Rouhunkoski, J.
- How do we Know Cognitive Interviewing is Any Good?; 2013; Willis, G. B.
- Survey optimisation considerations for Android, Apple and Windows 8 mobile devices; 2013; Owen, R.
- Speeding in Web Surveys: The tendency to answer very fast and its association with straightlining; 2013; Conrad, F. G.; Zhang, Che.
- About the Institute of Public Health - Data aspect; 2013; Zaletel, M.
- Analyzing Paradata to Investigate Measurement Error; 2013; Yan, T., Olson, K.
- Too Fast, Too Straight, Too Weird: Post Hoc Identification of Meaningless Data in Internet ; 2013; Leiner, D. J.
- Can timestamp analyses show the bottlenecks in web surveys?; 2013; Andreadis, I.
- Timing in a web based survey: an influential factor of the response rate; 2013; Paraschiv, D.-C.
- Achieving Synergy Across Survey Modes: Mail Contact and Web Responses from Address-Based Samples; 2013; Dillman, D. A.
- The Future of Social Media, Sociality, and Survey Research; 2013; Hill, C., Dever, J. A.
- Collecting Diary Data on Twitter; 2013; Richards, A., Dean, E., Cook, S.
- Second Life as a Survey Lab: Exploring the Randomized Response Technique in a Virtual Setting; 2013; Richards, A., Dean, E.
- Virtual Cognitive Interviewing Using Skype and Second Life; 2013; Dean, E., Head, B., Swicegood, J. E.
- Sentiment Analysis: Providing Categorical Insight into Unstructured Textual Data; 2013; Haney, C.
- Social Media, Sociality, and Survey Research; 2013; Hill, C., Dean, E., Murphy, J.