Web Survey Bibliography
Relevance & Research Question: Most ethical codes around the world require researchers to give some information to the respondent about the nature of the survey they are asking them to undertake. In traditional modes it was part of the job of the interviewer to ensure that people did not exclude themselves on the basis that they may not have views of interest (“your opinion counts!”), or that they may not ‘qualify’ (“all opinions count!”). Since we solicited people, they did not offer themselves to us, there were few ways they could self-select into a telephone or face-to-face study. With online research, and particularly access panel based research, respondents can actively choose to take part in any one of many invitations they receive, based on interest or perceived chances of qualification. The possibility of bias in interest in the subject matter questions the validity of the data collected and the projectability of the sample.
Methods & Data: We investigate three different categories, Pets, Automobiles and Sports. In each case we will be both specific about the item within the category (Dogs, Sports Cars and Football) and just use the general category heading. Experimental treatments reflect the solicitation methods available to us: the direct invitation that clearly mentions the subject matter; and using a router to direct willing respondents to a survey where they will be presented with an introduction stating the subject matter. Finally the survey is presented as an omnibus – a survey with many subjects – this will be both directly invited as well as being placed in the router, the survey here will contain all three subjects. Key dependent variables in the survey proper will be depth of interest in the subject matter.
Results: The survey has not been undertaken as yet. We do not foresee any issues completing the fieldwork and analysis since we would be utilizing our own resources.
Added Value: As a result of this research researchers will understand better the phenomenon of interest bias and how it can be overcome by proper use of modern solicitation methods and messages.
Web survey bibliography - 2014 (234)
- The relationship between nonresponse strategies and measurement error; 2014; Malhotra, N., Miller, J. M., Wedeking, J.
- Nonresponse and measurement error in an online panel; 2014; Roberts, C., Allum, N., Sturgis, P.
- Estimating the effects of nonresponses in online panels through imputation; 2014; Zhang, W.
- An empirical test of the impact of smartphones on panel-based online data collection; 2014; Drewes, F.
- Professional respondents in nonprobability online panels; 2014; Hillygus, D. S., Jackson, N. M., Young, M.
- Informing panel members about study results; 2014; Scherpenzeel, A., Toepoel, V.
- Determinants of the starting rate and the completion rate in online panel studies; 2014; Goeritz, A.
- The untold story of multi-mode (online and mail) consumer panels; 2014; McCutcheon, A. L., Rao, K., Kaminska, O.
- Online panels and validity; 2014; Groenlund, K., Strandberg, K.
- Assessing representativeness of a probability-based online panel in Germany; 2014; Struminskaya, B., Kaczmirek, L., Schaurer, I., Bandilla, W.
- A critical review of studies investigating the quality of data obtained with online panels based on...; 2014; Callegaro, M., Villar, A., Yeager, D. S., Krosnick, J. A.
- Online panel research: History, concepts, applications and a look at the future; 2014; Callegaro, M., Baker, R., Bethlehem, J., Goeritz, A., Krosnick, J. A., Lavrakas, P. J.
- Motives for joining nonprobability online panels and their association with survey participation behavior...; 2014; Keusch, F., Batinic, B., Mayerhofer, W.
- Improving web survey quality; 2014; Steinmetz, S., Bianchi, S. M., Tijdens, K. G., Biffignandi, S.
- WebSM Study: Survey Software in 2014; 2014; Vehovar, V., Cehovin, G., Mocnik, A.
- Design and Implementation of an Online Questionnaire Tool; 2014; Schaniel, R.
- The Influence of the Answer Box Size on Item Nonresponse to Open-Ended Questions in a Web Survey; 2014; Zuell, C., Menold, N., Koerber, S.
- What are the Links in a Web Survey Among Response Time, Quality, and Auto-Evaluation of the Efforts...; 2014; Revilla, M., Ochoa, C.
- Does Age Matter? The Influence of Age on Response Rates in a Mixed-Mode Survey; 2014; Gigliotti, L. M., Dietsch, A.
- Does the Choice of Header Images influence Responses? Findings from a Web Survey on Students’...; 2014; Barth, A.
- Methods and systems for managing an online opinion survey service; 2014; Mcloughlin, M. H., Seton, N., Blesy, K.
- Comparison of the quality estimates in a mixed-mode and a unimode design: an experiment from the European...; 2014; Revilla, M.
- Forget gamification; try writing a humanized survey; 2014; Pettit, A.
- Using respondent tweets to fill in survey gaps; 2014; Murphy, J.
- Using Paradata to Predict and to Correct for Panel Attrition in a Web-based Panel Survey; 2014; Rossmann, J., Gummer, T.
- Targeting the bias – the impact of mass media attention on sample composition and representativeness...; 2014; Steinmetz, S., Oez, F., Tijdens, K. G.
- Offline Households in the German Internet Panel; 2014; Bossert, D., Holthausen, A., Krieger, U.
- Which fieldwork method for what target group? How to improve response rate and data quality; 2014; Wulfert, T., Woppmann, A.
- Exploring selection biases for developing countries - is the web a promising tool for data collection...; 2014; Tijdens, K. G., Steinmetz, S.
- Evaluating mixed-mode redesign strategies against benchmark surveys: the case of the Crime Victimization...; 2014; Klausch, L. T., Hox, J., Schouten, B.
- The quality of ego-centered social network data in web surveys: experiments with a visual elicitation...; 2014; Marcin, B., Matzat, U., Snijders, C.
- Switching the polarity of answer options within the questionnaire and using various numbering schemes...; 2014; Struminskaya, B., Schaurer, I., Bosnjak, M.
- Measuring the very long, fuzzy tail in the occupational distribution in web-surveys; 2014; Tijdens, K. G.
- Social Media and Surveys: Collaboration, Not Competition; 2014; Couper, M. P.
- Improving cheater detection in web-based randomized response using client-side paradata; 2014; Dombrowski, K., Becker, C.
- Interest Bias – An Extreme Form of Self-Selection?; 2014; Cape, P. J., Reichert, K.
- Online Qualitative Research – Personality Matters ; 2014; Tress, F., Doessel, C.
- Increasing data quality in online surveys 4.1; 2014; Hoeckel, H.
- Moving answers with the GyroScale: Using the mobile device’s gyroscope for market research purposes...; 2014; Luetters, H., Kraus, M., Westphal, D.
- The effectiveness of recruitment strategies on general practitioner's survey response rates - a...; 2014; Pit, S. W., Pyakurel, S., Vo, T.
- Respondent-Driven Sampling of Heterosexuals at Increased Risk of HIV Infection; 2014; Batra, P., Gray, S. C., Krishna, N., Prachand, N., Robinson, W. T., Wejnert, C.
- Two Are Better Than One: The Use of a Mixed-Mode Data Collection to Improve the Electoral Forecast; 2014; de Rada, V. D., Pasadas del Amo, S.
- Social desirability is the same in offline, online, and paper surveys: A meta-analysis; 2014; Dodou, D., de Winter J. C. F.
- The impact of contact effort on mode-specific selection and measurement bias; 2014; Schouten, B., van der Laan, J., Cobben, F.
- Recent Books and Journals in Public Opinion, Survey Methods, and Survey Statistics; 2014; Callegaro, M.
- User-Generated Online Health Content: A Survey of Internet Users in the United Kingdom; 2014; Ziebland, S., Valderas, J., Lupianiez-Villanueva, F., O'Neill, B.
- Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information...; 2014; Schweiger, S., Oeberst, A., Cress, U.
- Social Media and Online Survey: Tools for Knowledge Management in Health Research ; 2014; Merolli, M., Sanchez, F. J. M., Gray, K.
- Using Online Social Media for Recruitment of Human Immunodeficiency Virus-Positive Participants: A Cross...; 2014; Yuan, P., Bare, M. G., Johnson, M. O., Saberi, P.
- Mobile Technologies for Conducting, Augmenting and Potentially Replacing Surveys: Report of the AAPOR...; 2014; Link, M. W., Murphy, J., Schober, M. F., Buskirk, T. D., Childs, J. H., Tesfaye, C.