Web Survey Bibliography
A core tenet of survey research is that the inferences one makes about the population can only be as good as the quality of the respondents in the sample. However, with declines in probability sample response rates and increases in non-probability Internet-based research, researchers have found it increasingly difficult to agree on the quality of a survey sample. Contributing to this difficulty is the variety of research studies that have evaluated the quality of survey data derived from probability-based and non-probability-based sources and the effectiveness of statistical methods to reduce error in data from
these sources. Specifically, some research has documented a greater average error among non-probability samples relative to probability samples (Chang & Krosnick, 2009; Yeager et al., in press), while other research has found few or small differences between the two. Other research has pointed to greater variability in results from surveys non-probability samples of Internet volunteers. For instance, Dedeker (2006) conducted the same study twice on the same Internet survey panel and reached two different business conclusions. An additional study found five to ten times greater variability in error among a sample of seven Internet surveys of non-probability samples versus seven probability sample surveys (Yeager et al., in press). Similarly-sized variability was found in the National Dutch Online Panel Comparison Study. Relatedly, statistical methods such as post-stratification survey weighting have inconsistent effects on non-probability sample surveys, and in some cases increase survey error. It is critically important to synthesize the survey accuracy studies summarized above as well as others. The present study will evaluate the evidence from more than 45 different studies have assessed the accuracy of non-probability sample surveys and the effectiveness of methods to improve their accuracy,
with the aim of helping researchers and consumers to have more informed expectations about data quality in their surveys.
Conference Homepage (abstract)
Web Survey Bibliography - Survey methodology (2746)
- Field Lessons From the Delivery of Questionnaires to Young Adults Using Mobile Phones; 2013; van Heerden, A. C., Norris, S. A., Tollman, S. M., Stein, A. D., Richter, L. M.
- Comparison of Smartphone and Online Computer Survey Administration; 2013; Wells, T., Bailey, J., Link, M. W.
- Panel Conditioning in Difficult Attitudinal Questions; 2013; Binswanger, J., Schunk, D., Toepoel, V.
- Web Coverage in the UK and its Potential Impact on General Population Web Surveys; 2013; Callegaro, M.
- Optimizing quality of response through adaptive survey designs; 2013; Schouten, B., Calinescu, M., Luiten, A.
- On the Impact of Response Patterns on Survey Estimates from Access Panels; 2013; Enderle, T., Muennich, R., Bruch, C.
- A Comparison of Data Quality Across Modes in a Mixed-Mode Collection of Administrative Records; 2013; Worthy, M., Mayclin, D.
- Reconceptualizing Survey Representativeness for Evaluating and Using Nonprobability Samples; 2013; Fan, D. P.
- To Click, Type, or Drag? Evaluating Speed of Survey Data Input Methods; 2013; Husser, J. A., Husser, J. A.
- Unit Nonresponse and Weighting Adjustments: A Critical Review; 2013; Brick, J. M.
- Web psychosocial surveys in cancer survivorship - a methodological note; 2013; Santin, O., Mills, M., Treanor, C., Mc Donald, G., Donnelly, M.
- Internet visual media processing: a survey with graphics and vision applications; 2013; Hu, S.-M., Chen, T., Xu, K., Cheng, M.-M., Martin, R. R.
- Measuring the impact of the Web: Rasch modelling for survey evaluation; 2013; Annoni, P., Weziak-Bialowolska, D., Farhan, H.
- Using Tablet Computers For “Intentional” Mobile Research; 2013; Seal, J.
- Encouraging Record Use For Financial Asset Questions In A Web Survey; 2013; Couper, M. P., Ofstedal, M. B., Lee, S.
- Experiments with methods to reduce attrition in longitudinal surveys; 2013; Fumagalli, L., Laurie, H., Lynn, P.
- Can Online Surveys Substitute Traditional Modes? An Error-Based Comparison of Online and On-Site Tourism...; 2013; Kim, N., Yu, X., Schwartz, Z.
- How incentives affect web-based survey response rates of athletic program donors; 2013; Alvarado, G., Callison, C.
- Investigating the Relationship among Prepaid Token Incentives, Response Rates, and Nonresponse Bias...; 2013; Parsons, N. L., Manierre, M. J.
- The Effect of Survey Mode on High School Risk Behavior Data: a Comparison between Web and Paper-based...; 2013; Raghupathy, S., Hahn-Smith, S.
- Web-Based Versus Traditional Paper Questionnaires: A Mixed-Mode Survey With a Nordic Perspective; 2013; Hohwue, L., Lyshol, H., Gissler, M., Hrafn Jonsson, S., Petzold, M., Obel, C.
- Going online with a face-to-face household panel: initial results from an experiment on the Understanding...; 2013; Jaeckle, A., Lynn, P., Burton, J.
- Targeted response inducement strategies on longitudinal surveys; 2013; Lynn, P.
- “Translating” between survey answer formats; 2013; Dolnicar, S., Grün, B.
- The Gamification of Marketing Researc; 2013; Donato, P., Link, M. W.
- Gamification Master Class; 2013; Puleston, J.
- Research Communities in Asia Pacific: A review of similarities and contrasts; 2013; Poynter, R.
- Measuring Up: Impact of mobile and segmentation on respondent behaviour; 2013; Luck, K.
- Best of Both Worlds? Can we make convenience samples representative?; 2013; Doe, P.
- Multimode, Global Scale Usage: Understanding respondent scale usage across borders and devices; 2013; Pettit, F. A., Courtright, M.
- Advanced Research Methods Training in the UK: Current Provision and Future Strategies; 2013; Moley, S., Wiles, R., Sturgis, P.
- Doing real time research: Opportunities and challenges; 2013; Back, L., Lury, C., Zimmer, R.
- ‘Digital Methods as Mainstream Methodology’: Building capacity in the research community...; 2013; Roberts, S., Hine, C., Morey, Y., Snee, H., Watson, H.
- New social media, new social science?; 2013; Woodfield, K., Morrell, G.
- Digital technology and data collection; 2013; Henriksen, B., Jewitt, C., Price, S., Sakr, M.
- Developing a New Mixed-Mode Methodology For a Provincial Park Camper Survey in British Columbia; 2013; Dyck, B. W.
- Mode effect analysis and adjustment in a split-sample mixed-mode Web/CATI survey; 2013; Kolenikov, S., Kennedy, C.
- Exploring factors associated with respondent mode choice for surveys using mobile devices.; 2013; Walton, L.
- Responsive design for mixed-mode panel data; 2013; Bianchi, A., Biffignandi, S.
- Adjusting for bias in a mixed-mode CAWI survey on University students ; 2013; Clerici, R., Giraldo, A.
- Comparative analysis of data from web and face-to-face surveys. A case study on e-commerce in young...; 2013; Cappello, C., Pellegrino, D.
- Insights into Action Profiling shopping occasions for retailers through mobile and online research; 2013; Churkina, O., Morris, T.
- Mobilizing your Branded Panel: Panel data quality during the smartphone transition; 2013; Kugel, C., Brien, D., Blechman, J.
- Evaluating the left‐right dimension: Category Selection Probing conducted in an online access...; 2013; Huefken , V.
- From Face‐to‐face to Web: Consequences for Measurement of Complex and Open‐ended Questions...; 2013; Villar, A., Fitzgerald, R., Martin, P., Harrison, E., Gatrrell, L.
- Computers, Tablet & Smart Phones: The Truth about Web‐based Surveys; 2013; Merle, P., Gearhart, S., Craig, C., Rahimi, M., Brooks, M. E., Vandyke, M.
- Explaining Interview Duration in Web Surveys on Political Attitudes and Behavior: A Multilevel Approach...; 2013; Gummer, T., Rossmann, J.
- An Examination of the Relationship Between Pretest Method Results and Data Quality; 2013; Maitland, A.
- Associations Between Interactional Indicators of Problematic Questions and Systems for Coding Question...; 2013; Dykema, J., Schaeffer, N. C., Garbarski, D.
- Online Survey Participation via Mobile Devices: implications for nonresponse; 2013; Poggio, T., Bosnjak, M.