Web Survey Bibliography
In the last two decades, Web or Internet surveys have had a profound impact on the survey world. The change has been felt mostly strongly in the market research sector, with many companies switching from telephone surveys or other modes of data collection to online surveys. The academic and public policy/social attitude sectors were a little slower to adopt, being more careful about evaluating the effect of the change on key surveys and trends, and conducting research on how best to design and implement Web surveys. The public sector (i.e., government statistical offices) has been the slowest to embrace Web surveys, in part because the stakes are much higher, both in terms of the precision requirements of the estimates and in terms of the public scrutiny of such data. However, National Statistical Offices (NSOs) are heavily engaged in research and development with regard to Web surveys, mostly notably as part of a mixedmode data collection strategy, or in the establishment survey world, where repeated measurement and quick turnaround are the norm. Along with the uneven progress in the adoption of Web surveys has come a number of concerns about the method, particularly with regard to the representational or inferential aspects of Web surveys. At the same time, a great deal of research has been conducted on the measurement side of Web surveys, developing ways to improve the quality of data collected using this medium. This seminar focuses on these two key elements of Web surveys — inferential issues and measurement issues. Each of these broad areas will be covered in turn in the following sections. The inferential section is largely concerned with methods of sampling for Web surveys, and the associated coverage and nonresponse issues. Different ways in which samples are drawn, using both non-probability and probability-based approaches, are discussed. The assumptions behind the different approaches to inference in Web surveys, the benefits and risks inherent in the different approaches, and the appropriate use of particular approaches to sample selection in Web surveys, are reviewed. The following section then addresses a variety of issues related to the design of Web survey instruments, with a review of the empirical literature and practical recommendations for design to minimize measurement error.
A total survey error framework (see Deming, 1944; Kish, 1965; Groves, 1989) is useful for evaluating the quality or value of a method of data collection such as Web or Internet surveys. In this framework, there are several different sources of error in surveys, and these can be divided into two main groups: errors of non-observation and errors of observation. Errors of nonobservation refer to failures to observe or measure eligible members of the population of interest, and can include coverage errors, sampling errors, and nonresponse errors. Errors of nonobservation are primarily concerned about issues of selection bias. Errors of observation are also called measurement errors (see Biemer et al., 1991; Lessler and Kalsbeeck, 1992). Sources of measurement error include the respondent, the instrument, the mode of data collection and (in interviewer-administered surveys) the interviewer. In addition, processing errors can affect all types of surveys. Errors can also be classified according to whether they affect the variance or bias of survey estimates, both contributing to overall mean square error (MSE) of a survey statistic. A total survey error perspective aims to minimize mean square error for a set of survey statistics, given a set of resources. Thus, cost and time are also important elements in evaluating the quality of a survey. While Web surveys generally are significantly less expensive than other modes of data collection, and are quicker to conduct, there are serious concerns raised about errors of non-observation or selection bias. On the other hand, there is growing evidence that using Web surveys can improve the quality of the data collected (i.e., reduce measurement errors) relative to other modes, depending on how the instruments are designed. Given this framework, we first discuss errors of non-observation or selection bias that may raise concerns about the inferential value of Web surveys, particularly those targeted at the general population. Then in the second part we discuss ways that the design of the Web survey instrument can affect measurement errors.
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Web survey bibliography (4086)
- Displaying Videos in Web Surveys: Implications for Complete Viewing and Survey Responses; 2017; Mendelson, J.; Lee Gibson, J.; Romano Bergstrom, J. C.
- Using experts’ consensus (the Delphi method) to evaluate weighting techniques in web surveys not...; 2017; Toepoel, V.; Emerson, H.
- Mind the Mode: Differences in Paper vs. Web-Based Survey Modes Among Women With Cancer; 2017; Hagan, T. L.; Belcher, S. M.; Donovan, H. S.
- Answering Without Reading: IMCs and Strong Satisficing in Online Surveys; 2017; Anduiza, E.; Galais, C.
- Ideal and maximum length for a web survey; 2017; Revilla, M.; Ochoa, C.
- Social desirability bias in self-reported well-being measures: evidence from an online survey; 2017; Caputo, A.
- Web-Based Survey Methodology; 2017; Wright, K. B.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
- Lessons from recruitment to an internet based survey for Degenerative Cervical Myelopathy: merits of...; 2017; Davies, B.; Kotter, M. R.
- Web Survey Gamification - Increasing Data Quality in Web Surveys by Using Game Design Elements; 2017; Schacht, S.; Keusch, F.; Bergmann, N.; Morana, S.
- Effects of sampling procedure on data quality in a web survey; 2017; Rimac, I.; Ogresta, J.
- Comparability of web and telephone surveys for the measurement of subjective well-being; 2017; Sarracino, F.; Riillo, C. F. A.; Mikucka, M.
- Achieving Strong Privacy in Online Survey; 2017; Zhou, Yo.; Zhou, Yi.; Chen, S.; Wu, S. S.
- A Meta-Analysis of the Effects of Incentives on Response Rate in Online Survey Studies; 2017; Mohammad Asire, A.
- Telephone versus Online Survey Modes for Election Studies: Comparing Canadian Public Opinion and Vote...; 2017; Breton, C.; Cutler, F.; Lachance, S.; Mierke-Zatwarnicki, A.
- Examining Factors Impacting Online Survey Response Ratesin Educational Research: Perceptions of Graduate...; 2017; Saleh, A.; Bista, K.
- Usability Testing for Survey Research; 2017; Geisen, E.; Romano Bergstrom, J. C.
- Paradata as an aide to questionnaire design: Improving quality and reducing burden; 2017; Timm, E.; Stewart, J.; Sidney, I.
- Fieldwork monitoring and managing with time-related paradata; 2017; Vandenplas, C.
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Interviewer Gender and Survey Responses: The Effects of Humanizing Cues Variations; 2017; Jablonski, W.; Krzewinska, A.; Grzeszkiewicz-Radulska, K.
- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
- Where, When, How and with What Do Panel Interviews Take Place and Is the Quality of Answers Affected...; 2017; Niebruegge, S.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance...; 2017; Wetzlehuetter, D.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- The Effect of Respondent Commitment on Response Quality in Two Online Surveys; 2017; Cibelli Hibben, K.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- Social Desirability and Undesirability Effects on Survey Response latencies; 2017; Andersen, H.; Mayerl, J.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.