Web Survey Bibliography
The term paradata refers to auxiliary data collected in a survey that describe the data collection process (Beaumont, 2005; Couper, 1998; Couper and Lyberg, 2005; Kreuter and Casas-Cordero, 2010; Kreuter, Couper, and Lyberg, 2010). Common examples include the number of calls made to a case, or interview duration. The technology available to today’s survey researcher has enabled the collection of large volumes of paradata in a nearly passive manner. Given this widespread collection of paradata, there are many research areas emerging that could inform both the collection of paradata and paradata-driven innovations for years to come. Motivated by a roundtable discussion at the 2011 Joint Statistical Meetings (JSM) and a recent Survey Practice article on this topic (Lynn and Nicolaas, 2010), this article reviews types of paradata, different ways that paradata are currently being used in practice, quality issues concerning paradata, and directions for future research.
Survey Practice (abstract) / (full text)
Web Survey Bibliography (6359)
- 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.
- Methods for eliminating skip statements from questionnaire logic; 2012; Canvanough Spencer, S.
- 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.
- Improving Survey Website Usability ; 2012; Vannette, D.
- Specialized Tools for Measuring Past Events ; 2012; Belli, R. F.
- Transparency, Access and the Credibility of Survey Research; 2012; Lupia, A.
- Experience Sampling and Ecological Momentary Assessment; 2012; Stone, 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.
- Can Official Records Correct Errors in Turnout Self-reports?; 2012; Berent, M., Krosnick, J. A., Lupia, A.
- 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.
- Probability vs. Non-probability Methods; 2012; Langer, G,
- 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.
- Sampling for Single and Multi-Mode Surveys using Address-Based Sampling; 2012; O'Muircheartaigh, C.
- What Human Language Technology can do for you (and vice versa); 2012; Liberman, M.
- Proxy Reporting; 2012; Cobb, C. L.
- The Impact of Survery Nonresponse on Survey Accuracy; 2012; Keeter, S.
- How accurate are surveys of objective phenomena?; 2012; Chang, L. C., Krosnick, J. A.
- An Empirical Investigation of the Role of the Email Contact in Web Survey Response Rates; 2012; Hsu, H.-Y., Lai, Y.-H., Chin, H.-Y.
- 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.
- The re-engineering of the Structural Earnings survey process: Mixed - Mode data collection and new E...; 2012; Cardinaleschi, S., De Santis, S., Rocci, F., Spinelli, V.
- 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.
- Does mentioning "Some People" and "Other People" in an opinion question improve...; 2012; Yeager, D. S., Krosnick, J. A.
- Digital Divides: A connectivity continuum for the United States. Data from the 2011 Current Population...; 2012; File, T.
- Developments and the impact of smart technology; 2012; Macer, 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.

