Web Survey Bibliography
Data collection is at the basis of quality in statistics. Technological developments make new data collection methods available, whereas others lose importance. While the Internet is already an established mode to collect data from businesses, only recently national statistical institutes have started considering web data collection for surveys among persons and households. At the moment several countries are developing Internet data collection for social surveys. There are two main reasons behind this innovation. The first is that Internet data collection is cheaper than traditional methods. The second reason is that many countries face decreasing response rates. Internet data collection could be helpful to stop this trend. A further reason, related to the last one, is that the public ask for it. An increasing number of persons have no problems participating in a survey as such, but do not like an interviewer at their door or on the telephone. They prefer to fill out the questionnaire when it is convenient for them. These drivers behind the development will probably gain strength in the near future, resulting in an acceleration of the process.
This discussion paper reflects on the possibilities to use web data collection for official statistics in household surveys and how this innovation process could be organised efficiently on EU level. The Labour Force Survey (LFS) is used as a case to illustrate challenges and potential pitfalls. It is a large scale survey with a high level of harmonisation among the Member States and therefore the potential cost savings are substantial. Furthermore, the LFS is already designed as a mixed mode survey in many countries; this allows the smooth introduction of a new data collection mode. And finally, the LFS plays a central role in the system of social statistics.
The paper focuses on the challenges ahead in official statistics regarding web data collection. At this stage, only major issues to address and organisational steps to be taken can be presented. It is still too early to give solid solutions or identify best practices.
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Web Survey Bibliography - Conferences, workshops, tutorials, presentations (2831)
- Battle of the Scales: Understanding Respondent Scale Usage in the US and Abroad; 2013; Courtright, M., Pashupati, K., Pettit, F. A.
- Modular Survey Design: A Bite Size Proposal; 2013; Kelly, F., Stevens, S., Johnson, A.
- Cyborgs vs. Monsters: Assembling Modular Surveys to Create Complete Datasets; 2013; Johnson, E. P., Siluk, L., Tarraf, S.
- Do I Have Your Full Attention?; 2013; Cape, P. J.
- Does Sample Size Still Matter?; 2013; Bakken, D. G., Bond, M.
- Optimizing Surveys for Smartphones: Maximizing Response Rates While Minimizing Bias; 2013; Lattery, K., Park Bartolone, G., Saunders, T.
- Shorter Isn't Always Better; 2013; Burdein, I.
- Solving the Unintentional Mobile Challenge; 2013; Peterson, G., Mechling, J., LaFrance, J., Ham, G.
- Mobile Research Risk: What Happens to Data Quality When Respondents Use a Mobile Device for a Survey...; 2013; Baker-Prewitt, J.
- Pros and cons of virtual interviewers – vote in the discussion about surveytainment; 2013; Póltorak, M., Kowalski, J.
- The fish model: What factors affect participants while filling in an online questionnaire?; 2013; Mohamed, B., Lorenz, A., Pscheida, D.
- Interview Duration in Web Surveys: Integrating Different Levels of Explanation; 2013; Rossmann, J., Gummer, T.
- The monetary value of good questionnaire design; 2013; Tress, F.
- Technical and methodological meta-information on current practices in online research: A full population...; 2013; Burger, C., Stieger, S.
- Using interactive feedback to enhance response quality in Web surveys. The case of open-ended questions...; 2013; Emde, M., Fuchs, M.
- Reducing Response Order Effects in Check-All-That-Apply Questions by Use of Dynamic Tooltip Instructions...; 2013; Kunz, T., Fuchs, M.
- Slide to ruin data: How slider scales may negatively affect data quality and what to do about it; 2013; Funke, F.
- Measuring wages via a volunteer web survey – a cross-national analysis of item nonresponse; 2013; Steinmetz, S., Annmaria, B.
- Identifying and Mitigating Satisficing in Web Surveys: Some Experimental Evidence; 2013; Blumenstiel, J. E., Rossmann, J.
- Does one really know?: Avoiding noninformative answers in a reliable way.; 2013; de Leeuw, E. D., Boevee, A., Hox, J.
- Online Mixed Mode Surveying using a Responsive Design; 2013; Kissau, K.
- Sensitive Topics in PC and Mobile Web Surveys; 2013; Mavletova, A. M., Couper, M. P.
- Mobile Research Performance: How Mobile Respondents Differ from PC Users Concerning Interview Quality...; 2013; Schmidt, S., Wenzel, O.
- Who responds to website visitor satisfaction surveys?; 2013; Andreadis, I.
- Measuring working conditions in a volunteer web survey; 2013; de Pedraza, P., Villacampa, A.
- Sampling online communities: using triplets as basis for a (semi-) automated hyperlink web crawler.; 2013; Veny, Y.
- Prison break: Releasing offline experiments from methodological constraints by transforming them into...; 2013; Förstel, H., Manthei, K., Mohnen, A., Berger, G.
- Comparison of psychometric properties of internet versions of the Marlowe-Crowne Social Desirability...; 2013; Vesteinsdottir, V., Reips, U. -D., Joinson, A. N., Porsdottir, F.
- Why are you leaving me?? - Personality predictors of answering drop out in an online-study; 2013; Thielsch, M., Nestler, S., Back, M.
- Propensity Score Weighting – Can Personality Adjust for Selectivity?; 2013; Glantz, A., Greszki, R.
- Research Design as an Influencing Factor for Reliability in Online Market Research; 2013; Wengrzik, J., Theuner, G.
- Ethics, privacy and data security in web-based course evaluation; 2013; Salaschek, M., Meese, C., Thielsch, M.
- Seducing the respondent – how to optimise invitations in on-site online research?; 2013; Póltorak, M., Kowalski, J.
- Influence of mobile devices in online surveys; 2013; Maxl, E., Baumgartner, T.
- E-questionnaire in cross-sectional household surveys; 2013; Karaganis, M.
- GESIS Online Panel Pilot: Results from a Probability-Based Online Access Panel; 2013; Kaczmirek, L., Bandilla, W., Schaurer, I., Struminskaya, B., Weyandt, K.
- Online Survey – Research with children on advertising impact; 2013; Funkenweh, V., Busch, J., Amthor, A. L., Boeer, A., Gaedke, J.
- HTML5 and mobile Web surveys: A Web experiment on new input types; 2013; Funke, F.
- Metadata on the demographics of online research: Results from a full-range study of available online...; 2013; Burger, C., Stieger, S.
- How the screen-out influence the dropout of a commercial panel; 2013; Bartoli, B.
- Beyond methodology - some ethical implications of "doing research online"; 2013; Heise, N.
- Innovation in Data Collection: the Responsive Design Approach; 2013; Bianchi, A., Biffignandi, S.
- Break-off and attrition in the GIP amongst technologically experienced and inexperienced participants...; 2013; Blom, A. G., Bossert, D., Clark, V., Funke, F., Gebhard, F., Holthausen, A., Krieger, U., Wachenfeld...
- Nonresponse and Nonresponse Bias in a Probability-Based Internet Panel; 2013; Blom, A. G., Bossert, D., Funke, F., Gebhard, F., Holthausen, A., Krieger, U.
- Rewards - Money for Nothing?; 2013; Cape, P. J., Martin, P.
- Effects of incentive reduction after a series of higher incentive waves in a probability-based online...; 2013; Struminskaya, B., Kaczmirek, L., Schaurer, I., Bandilla, W.
- Timing of Nonparticipation in an Online Panel: The effect of incentive strategies; 2013; Douhou, S., Scherpenzeel, A.
- Mixed-mode including web: Recent developments at Statistics Netherlands; 2013; Luiten, A., Schouten, B.
- Web coverage in the UK and its potential impact on general population web surveys; 2013; Callegaro, M.
- Surveys on Mobile Devices: Opportunities and Challenges; 2013; Couper, M. P.