Web Survey Bibliography
The rise in Smartphone use and the advances in app and internet use on such devices create a viable mode for survey data collection that now needs to be formally investigated. The Smartphone offers a multimode device that can be accessed via voice, text or internet and can make use of synchronous multimedia messaging. As form factors continue to increase in size, these handheld devices may also replace handheld personal data assistants for in-person data collection. Clearly best practices and current research related to online survey development can serve as a basis for such design and implementation. However, differences in processing, form factors and variability among platforms suggest that these best practices must be adapted, expanded and modified for surveys on mobile devices. The first part of this paper presents comprehensive results from one of the first smartphone mode experiments (The Got Healthy Apps Study) conducted within the U.S. In particular, this study randomized iPhone owners who were members of an online computer web-panel to complete the survey about health behaviors and health related app usage via their iPhone (221 completes) or computer (209 completes). We report differences in survey completion times, recency/primacy effects, back button use frequency, open-ended data entry and overall app and smartphone usage across the modes.
The second part of this paper focuses on one of the defining features of smartphones – the applications (APPS). Currently, health, survey, market and other researchers have begun to deploy smartphone apps as a means of intervention, data collection and marketing. What has lagged behind in the research literature is how smartphone users refer to, describe or recognize the apps they use- either by icon or by name? This feature is important if phone surveys are to be deployed as a mode of inquiry regarding smartphone app use (i.e. how should interviewers refer to these apps- by description or by name?). The second part of this study presents the results from a within-person app recognition experiment that randomized respondents to one of two presentation orders (app icon images followed by app names or vice-versa) and to one of two app lists (each contained eight of the most popular apps from overall, lifestyle and game categories). Using a mixed effects model, we examine differences in app recognition rates by mode across these two random effects. We also provide a brief description of how the iPhone survey was developed and deployed.
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Web survey bibliography - 2012 (371)
- The Impact of Academic Sponsorship on Online Survey Dropout Rates; 2012; Allen, P. J., Roberts, L. D.
- Especially for You: Motivating Respondents in an Internet Panel by Offering Tailored Questions; 2012; Oudejans, M.
- Social media as a data collection tool: the impact of Facebook in behavioural research; 2012; Zoppos, E.
- Smartphone Apps and User Engagement: Collecting Data in the Digital Era; 2012; Link, M. W.
- Snowball Sampling in Online Social Networks; 2012; Raissi, M., Ackland, R.
- The Use of Facebook as a Locating and Contacting Tool; 2012; McCarthy, T.
- How Often Do You Use the App with a Bird on It? Exploring Differences in Survey Completion Times, Primacy...; 2012; Buskirk, T. D.
- Data quality of questions sensitive to social-desirability bias in web surveys; 2012; Lozar Manfreda, K., Zajc, N., Berzelak, N., Vehovar, V.
- Online Questionnaires: Development of ‘basic requirements’; 2012; Tries, S., Blanke, K.
- Social research in online context: methodological reflections on web surveys from a case study; 2012; Pandolfini, V.
- Efficacy of a health-related Facebook social network site on health-seeking behaviors; 2012; Woolley, P., Peterson, M.
- The war against unengaged online respondents; 2012; Gittelman, S. H., Trimarchi, E.
- Qualitatively Speaking: The five absolute, no-excuse must-dos for online qualitative researchers; 2012; Rossow, A.
- By the Numbers: Lessons for using online panels in B2B research; 2012; Elsner, N.
- Specialized Tools for Measuring Past Events ; 2012; Belli, R. F.
- Transparency, Access and the Credibility of Survey Research; 2012; Lupia, A.
- Can Microtargeting Improve Survey Sampling? An Assessment of Accuracy and Bias in Consumer File Marketing...; 2012; Pasek, J.
- Anonymity and Confidentiality; 2012; Tourangeau, R.
- Cognitive Evaluation of Survey Instruments: State of the Science (Art?) and Future Directions; 2012; Willis, G. B.
- Oh, Just One More Thing … Leveraging “Leave-Behinds” in Data Collection; 2012; Link, M. W.
- Paradata; 2012; Kreuter, F.
- Computation of Survey Weights: Bridging Theory and Practice; 2012; DeBell, M.
- Optimizing Response Rates; 2012; Brick, J. M.
- Modes of Data Collection; 2012; Tourangeau, R.
- The Use and Effects of Incentives in Surveys; 2012; Singer, E.
- Improving Question Design to Maximize Reliability and Validity; 2012; Krosnick, J. A.
- Respondent Attrition vs Data Attrition and Their Reduction; 2012; Olsen, R. J.
- Survey Interviewing: Deviations from the Script; 2012; Schaeffer, N. C.
- How accurate are surveys of objective phenomena?; 2012; Chang, L. C., Krosnick, J. A.
- Measure the response burden in the Swedish Intrastat system; 2012; Weideskog, F.
- Mode and non-response effects and their treatment; 2012; Chrysanthopoulos, S., Georgostathi, A.
- What can be said about quality in the Central Population Register based on a self-completion survey...; 2012; Falnes-Dalheim, E., Pedersen, H. E.
- Improving the quality of complex surveys: The case of the EU Labour Force Survey ; 2012; van der Valk, J.
- Pros and cons of Internet based User Satisfaction Surveys; 2012; Consoli, A., Matsulevits, L.
- Between demand and reality: Ensuring efficiency and quality in pretesting questionnaires; 2012; Sattelberger, S., Blanke, K.
- How to provide high data quality in online-questionnaires: Setting guidelines in design; 2012; Tries, S., Nebel, S., Blanke, K.
- Boosting Web pick-up Rates by referring to Compliance Principles ; 2012; Falnes-Dalheim, E., Haraldsen, G., Sundvoll, A.
- Choosing a Data Collection Approach: Mixed Mode Design Experiences in Statistics Finland; 2012; Taskinen, P., Kiianmaa, N.
- Ebook readings jumps, print book reading declines; 2012; Rainie, L., Duggan, M.
- Digital Divides: A connectivity continuum for the United States. Data from the 2011 Current Population...; 2012; File, T.
- How Should Debriefing Be Undertaken in Web-Based Studies? Findings From a Randomized Controlled Trial...; 2012; McCambridge, J., Kypri, K., Wilson, A.
- Better customer in sight in real time; 2012; Macdonald, E., Wilson, H. N., Konus, H.
- Best practices in data cleaning: A complete guide to everything you need to do before and after collecting...; 2012; Osborne, J. W.
- Benchmarking for better surveys; 2012; Nallan, S.
- Adult gadget ownership over time (2006-2012); 2012
- Subjective Well-being Of Spanish Workers: Continuous Voluntary Web Survey Examination; 2012; de Pedraza, P., Guzi, M.
- Specific mixed-mode methodology to reach sensory disabled people in quantitative surveys; 2012; Fontaine, S.
- Response Mode Choice and the Hard-to-Interview in the American Community Survey; 2012; Nichols, E. M., Horwitz, R., Guarino Tancreto, J.
- Recruiting in an Internet panel using respondent driven sampling; 2012; Schonlau, M.
- A Choice in Mode: A Solution for Increasing Response Rates of Hard-to-Survey Populations?; 2012; Haan, M., Ongena, Y. P.