Web Survey Bibliography
Response latency to web surveys is of considerable interest. Time from the stimulus (here, displaying a question) to the response (here, recording the answer) is used to identify potentially problematic respondents (those with low latency) and items (those with high latency). Such uses are, however, typically narrowly constructed. We analyze a wide variety of factors using a rich dataset to develop a deeper understanding of the drivers of response latency in web surveys. The Rice University Religion and Science in International Context (RASIC) survey of members of biology and physics departments in Italian universities and research institutes measured response latency for each survey item. The RASIC dataset is a rich source of material. Respondent-level measures include extensive biographical data including age, academic rank, and language of choice (the survey was offered in Italian and English). Item-level measures include length of item, reading difficulty, topic, number of responses, and position in survey. Paradata include accumulated time spent on the survey, time of day, and device/browser used. The resulting dataset has respondent x item observations, with each observation being nested within respondent (e.g., age, tenure) and item (e.g., item length, reading grade level). Due to this nesting, a hierarchical cross-classified model is used for analysis. Our findings will shed light on the impact of a broad range of factors associated with response latency, addressing questions including the effects of time of day, age, means of access, reading grade level, number of response options, and so on. These analyses will provide important context for the perhaps simplistic interpretations of response latency: low latency being a desirable trait for items but undesirable for a respondent. Data collection utilized for this paper was funded by the Templeton World Charity Foundation, grant TWCF0033.AB14, Elaine Howard Ecklund, PI, Kirstin RW Matthews and Steven W. Lewis co-PIs.
Web survey bibliography (4086)
- Facebook as a Tool for Respondent Tracing; 2015; Schneider, S. J., Burke-Garcia, A., Thomas, G.
- Social Science Survey Methodology Training: Understanding the Past and Assessing the Present to Shape...; 2015; Jans, M., Meyers, M., Fricker, S.
- Internet Research in Psychology; 2015; Gosling, S. D., Mason, W.
- Handbook of Health Survey Methods; 2015; Johnson, T. P. (Ed.)
- Adapting an interviewer - administered survey for web self - completion in a mixed - mode design ; 2015; Betts, P.; Cubbon, B.
- Future Training of Survey Methodologists; 2015; Kolenikov, S., Jans, M., O'Hare, B. C., Fricker, S.
- Automatic data collection on the Internet (web scraping); 2015; Boettcher, I.
- The Impact of Survey Mode (Mail versus Telephone) and Asking About Future Intentions; 2015; Beebe, T. J.
- Offline recruiting of young people for an online survey - what affects response rates; 2015; Zeglovits, E.
- Finding Item Nonresponse Patterns: Three Internet Survey Experiments Into the Effects of Nonresponse...; 2015; Van De Maat, J.
- Placement of the Linkage Consent Question in a Web Survey of Establishments; 2015; Sakshaug, J. W.; Vicari, B.
- The effectiveness of incentives on recruitment and retention rates: an experiment in a web survey; 2015; Mulder, J.; Douhou, S.
- Using WhatsApp as a Survey Tool; 2015; Ongena, Y. P.; Haan, M.
- The Effects of Adding a Mobile-Compatible Design to the American Life Panel; 2015; Toepoel, V.; Lugtig, P. J.; Amin, A.
- Technology and Reporting of Daily Activities – Considerations for Analysis of Behaviours in Mixed...; 2015; Fisher, K.; Gershuny, J.
- Does the Use of Mobile Devices (Tablets and Smartphones) Affect Survey Quality and Choice Behaviour...; 2015; Glenk, K.; Liebe, U.; Oehlmann, M.
- Smartphones @work; 2015; Bittman, M.
- Measurement Error in Discontinuous Online Survey Panels: Panel Conditioning and Data Quality; 2015; Atkeson, L. R.; Adams, A. N.; Karp, J. A.
- Cheating in web surveys. Evidence from a split-ballot repeated experiment on knowledge questions on...; 2015; Ladini, R.; Vezzoni, C.
- Does Personalized Feedback Increase Respondent Motivation?; 2015; Kroh, M.; Kuhne, S.
- Adapting Grid Questions for Mobile Devices; 2015; de Bruijne, M.; Das, M.; van Soest, A.; Wijnant, A.
- Unplanned use of mobile devices in a probabilistic online panel survey: Patterns of use and implications...; 2015; Poggio, T.; Bosnjak, M.; Bandilla, W.; Weyandt, K.
- The importance of scale direction between different modes; 2015; Agalioti-sgompou, V.
- Impact of response scale direction on survey responses in web and mobile web surveys; 2015; Yan, T.; Keusch, F.
- Comparing response order experiments with probability and non-probability samples; 2015; Yeager, D. S.; Krosnick, J. A.; Silber, H.
- Direction of Response Format in Web and Paper & Pencil Surveys; 2015
- Comparison of different mixed-mode and face - to face surveys - response rates and costs; 2015; Ainsaar, M.; Hendrikson, R.
- Nonresponse and Measurement Bias in Web surveys ; 2015; Metzler, A.; Fuchs, M.
- Correlates of early and late responses to surveys in an online panel; 2015; Douhou, S.; Vis, C.
- Higher Item Nonresponse Rates Caused by Slider Scales in Web Surveys; 2015; Toepoel, V.; Funke, F.
- The effect of response formats on data quality and comparability across online PC and smartphone surveys...; 2015; Cleary, A.; Allum, N.; Kolbas, V.
- Mobile devices in a web panel: what are the results of adjusting questionnaires for smartphones and...; 2015; de Bruijne, M.; Wijnant, A.
- Online Eye-Tracking of Dynamic Advertising Content in (Mobile) Web-Surveys; 2015; Berger, S.
- Deep impact or no impact, evaluating opportunities for a new question type: Statement allocation on...; 2015; Schmidt, S.
- Approaches for Evaluating Online Survey Response Quality; 2015; Gluck, N.
- Coding Surveys on their Item Characteristics: Reliability Diagnostics; 2015; Bais, F.; Schouten, B.; Toepoel, V.
- Predicting Response Times in Web Surveys; 2015; Wenz, A.
- Positioning of Clarification Features in Open Frequency and Open Narrative Questions; 2015; Fuchs, M.; Metzler, A.
- The Role of Device Type and Respondent Characteristics in Internet Panel Survey Breakoff; 2015; McCutcheon, A. L.
- Web Survey Invitations: Design Features to Improve Response Rates; 2015; Hughes, J.; Marlar, J.
- Advance Postcard Mailing Improves Web Panel Survey Participation; 2015; Bertoni, N.; Burkey, A.; Caldaro, M.; Keeter, S.; DiSogra, C.; McGeeney, K.
- Mobile Devices for the Collection of Sensitive Information; 2015; Maitland, A.; Mercer, A. W.; Tourangeau, K.; Williams, Do.
- What Is The Impact of Smartphone Optimization on Long Surveys?; 2015; Cole, J.; Brooks, K.; Sarraf, S.
- Examining the Impact of Mobile First and Responsive Web Design on Desktop and Mobile Respondents; 2015; Tharp, D.
- Can An Importance Prompt Reduce Item Nonresponse For Demographic Items Across Web and Mail Modes?; 2015; Israel, G. D.
- Leveraging Area Probability Sampling in Recruiting Households for Web Surveys; 2015; Copeland, K.; Pedlow, K.; Tupek, A.
- Reducing Coverage Error in a Web Survey of College Students; 2015; Daley, K.; Pacer, J.
- Influences on Response Latency in a Web Survey; 2015; Ackermann, A.; Cheng, H. W.; Howard Ecklund, E.; Kolenikov, S.; Phillips, B. T.
- App vs. Web for Surveys of Smartphone Users; 2015; Igielnik, R.; McGeeney, K.
- Where Does the Platform Matter: The Impact of Geographic Clustering in Device Ownership and Internet...; 2015; Bilgen, I.; English, N.; Stern, M. J.; Ventura, I.