Web Survey Bibliography
Incentives are material and nonmaterial inducements and rewards that are offered to respondents in exchange for their participation in studies. This chapter explains the advantages and disadvantages of using incentives in Web-based studies and describes the types of incentives that are available. Moreover, the chapter seeks to develop evidence-based guidelines for short-term, as well as long-term, use of incentives to attain the goal of collecting high-quality data in a cost-conscious manner. Although a number of theoretical frameworks have been proposed to explain how incentives work (e.g., for an overview of theoretical accounts, see Singer, 2002), the focus of this chapter is pragmatic rather than theoretical. By handing out incentives to respondents, researchers can increase the likelihood of people participating in Web-based studies, and incentives may improve the quality of respondents responses. In particular, incentives can increase the response and the retention rates in a study. The response rate is the number of people who call up the first page of a study divided by the number of people who were invited or were aware of and eligible to take part in this study. The retention rate is the number of respondents who stay until the last page of a study relative to the number of respondents who have called up the first page of this study. Moreover, there is the hopebut not yet many datathat incentives will also increase other facets of data quality such as the completeness, consistency, and elaborateness of participants answers. However, using incentives might also entail undesirable effects. First, incentives might increase the response and retention rates at the expense of other facets of data quality, for example, more items are skipped; response styles occur more often; or answers to open-ended questions are shorter. It is possible that groups who are offered an incentive will answer less conscientiously than groups without incentives because the incentives might reduce the intrinsic motivation to perform the task (Heerwegh, 2006). Howeverat least in offline surveyssometimes the opposite has been found to be the case (Singer, Van Hoewyk, & Maher, 2000). Singer (2002) found that people who are rewarded for their participation would continue to give good information (p. 168). The second potential undesirable effect of incentives is that they might attract a particular type of respondent and thereby bias sample composition (e.g., poorer people may be more responsive than richer people to monetary incentives; Groves & Peytcheva, 2008). The third possible effect is that incentives might actually reduce the response and retention rates by alienating intrinsically motivated volunteers (see Deci, 1971). Finally, incentives might bias the study results, for example, by altering the mood of the respondents (Singer, 2002) or by altering respondents attitude to the researcher. There is the risk that to earn an incentive, people with little motivation will fill in meaningless data to get to the end of a survey quickly. When no incentive is promised, bored people usually abandon the study prematurely, so they are easily identifiable. Moreover, when offering incentives, researchers need to follow ethical guidelines as well as legal regulations (see chap. 16, this volume). Because the laws pertaining to the use of incentives differ across some countries, particular care is necessary with international studies. Finally, in studies with ad hoc recruitment of respondents, incentives might induce some people to fill out and submit the questionnaire many times, and it is not always possible to detect skillful fraud. To weigh whether incentives can be recommended despite possible drawbacks, researchers need to know how large the desirable and undesirable effects are. (PsycINFO Database Record (c) 2010 APA, all rights reserved). (from the chapter)
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Web survey bibliography - 2010 (251)
- Running experiments on Amazon Mechanical Turk; 2010; Paolacci, G., Chandler, J., Ipeirotis, P. G.
- Making Good Use of Survey Paradata; 2010; Lynn, P., Nicolaas, G.
- Questionnaire Length, Fatigue Effects and Response Quality Revisited; 2010; Cape, P. J.
- Preventing Satisficing in Online Surveys: A “Kapcha” to Ensure Higher Quality Data...; 2010; Chandler, D., Kapelner, A.
- Game on; 2010; Ewing, T.
- Respondent Engagement: How Much Does it Matter?; 2010
- The Internet and Social Inequalities; 2010; Mannon, S.E.; Witte, J. C.
- Need to Improve Routine HIV Testing of U.S. Veterans in Care: Results of an Internet Survey; 2010; Valdiserri, R. O., Nazi, K., McInnes, D. K., Ross, D., Kinsinger, L.
- The Prevalence of Chronic Pain in United States Adults: Results of an Internet-Based Survey; 2010; Johannes, C. B., Le, T. K., Zhou, X., Johnston, J. A., Dworkin, R. H.
- Response Rates in Organizational Science, 1995–2008: A Meta-analytic Review and Guidelines for...; 2010; Anseel, F., Lievens, F., Schollaert, E., Choragwicka, B.
- Marketing Research: Methodological Foundations; 2010; Iacobucci, D., Churchill, G.A. Jr.
- Computer Assisted Interview Testing Tool (CTT) - a review of new features and how the tool has improved...; 2010; Stark, R., Gatward, R.
- Address-based Sampling Nets Success for KnowledgePanel® Recruitment and Sample Representation; 2010; DiSogra, C.
- A method of automated nonparametric content analysis for social science; 2010; Hopkins, D. J., King, G.
- Developing a web explicit research strategy theory in African universities: a cross-comparison of specific...; 2010; Kirigha, K. A., Neema-A.
- The use of paradata to monitor and manage survey data collection; 2010; Kreuter, F., Couper, M. P., Lyberg, L. E.
- Mitigating Online Survey Nonresponse Error In Aviation Research; 2010; Ison, D. C.
- Optimizing response rates in online surveys; 2010; Kaczmirek, L.
- The Decision Maker's Guide to Online Research; 2010
- Mixed-Method Approaches to Social Network Analysis; 2010; Edwards, G.
- Measuring Intent to Participate and Participation in the 2010 Census and Their Correlates and Trends...; 2010; Pasek, J., Krosnick, J. A.
- Nonresponse and Measurement Error in Mobile Phone Surveys ; 2010; Kennedy, C.
- Wordle; 2010; Feinberg, J.
- What it takes to be a top 100 website; 2010
- Total Survey Error: past, present, and future; 2010; Groves, R. M., Lyberg, L. E.
- There is an app for that! A review of smartphone apps for marketing research; 2010; Michelson, M.
- The who, what, and where of America: Understanding the American Community Survey; 2010; Gaquin, D. A.
- The weirdest people in the world?; 2010; Heine, S. J., Henrich, J., Norenzayan, A.
- The state of online research in the U.S.; 2010; Miller, J.
- The psychology or survey response. An ASA webinar; 2010; Tourangeau, R.
- The psychology of survey response, 2nd Edition; 2010; Tourangeau, R., Bradburn, N. M.
- The multidimensional integral business survey response model; 2010; Bavdaz, M.
- The impact of next and back buttons on time to complete and measurement reliability in computer-based...; 2010; Hays, R. D., Bode, R., Rothrock, N., Riley, W., Cella, D., Gershon, R.
- The Gallup Poll: Public opinion 2009; 2010; Gallup, A. M.
- Surveying cultures: Discovering shared conceptions and sentiments; 2010; Heise, D. R.
- Site-intercpet survey best practices; 2010; Henning, J.
- Sampling: design and analysis, 2nd Edition; 2010; Lohr, S. L.
- Research synthesis. AAPOR report on online panels; 2010; Brick, J. M., Baker, R., Blumberg, S. J., Couper, M. P., Courtright, M., Dennis, J. M., Dillman, D....
- Recruiting probability samples for a multi-mode research panel with Internet and mail components; 2010; Rao, K.
- Real ID. State of The Art Representative and Repeatable Online Samples. Behaviorally Profiled Respondents...; 2010; Gittelman, S. H., Trimarchi, E.
- Randomized response and indirect questioning techniques in surveys; 2010; Chaudhuri, A.
- Protecting and accessing data from the survey of earned doctorates: A workshop summary; 2010; Plewes, T. J.
- Paradata: a new data source from web-administered measures; 2010; Sowan, A. K., Jenkins, L. S.
- Overview of data collection methodology; 2010
- On-the-go and in-the-moment. Mobile research offers speed, immediacy; 2010; Pferdekamper, T.
- Mixed-mode surveys; 2010; Dillman, D. A., Messer, B. L.
- Measuring the group quarters population in the American Community Survey: Interim report; 2010; Marton, K., Voss, P. R.
- Measures of interobserver agreement and reliability; 2010; Shoukri, M. M.
- Machines that lean how to code open ended survey data; 2010; Esuli, A., Sebastiani, F.
- Libraries nationwide receiving ALA-APA Library Salary Survey Invitation; 2010; Grady, J.