Web Survey Bibliography
Title Characterizing Satisficers in Web Surveys Using Paradata to Target Interventions
Author Vetting, S. S.; Horwitz, R.; Bray, R.; Hernandez Vivier, A.; Tancreto, J.; Reiser, C.
Year 2016
Access date 06.06.2016
Abstract
Surveys are increasingly offering web as part of a mixed mode design to elicit survey data from respondents. One concern with this approach is that
respondents, in the absence of an interviewer, must be sufficiently motivated to provide accurate, complete, and honest data. Respondents that do not put forth the effort to ensure the accuracy of their responses engage in what survey researchers have dubbed satisficing behavior. Satisficing behavior can lead to data quality issues. Past studies have shown the utility of incorporating both proactive and reactive interventions into survey designs to prevent or discourage this behavior (Conrad et al., 2011; Zhang, 2013). In this study, we use the 2013 National Survey of College Graduates (NSCG) web paradata to classify respondents based on their tendency to satisfice. Specifically, we use factors such as response time, non-differentiation among answers, omissions, and early termination to identify degrees of satisficing. We also look for correlations between satisficing behaviors and demographic characteristics to help characterize these respondents. Successful classification is instrumental in targeting interventions planned for future studies based on the type of satisficing. Conrad, F. G., Tourangeau, R., Couper,M. P., & Zhang, C. (2011). Interactive Interventions in Web Surveys Can Increase Response Accuracy.
respondents, in the absence of an interviewer, must be sufficiently motivated to provide accurate, complete, and honest data. Respondents that do not put forth the effort to ensure the accuracy of their responses engage in what survey researchers have dubbed satisficing behavior. Satisficing behavior can lead to data quality issues. Past studies have shown the utility of incorporating both proactive and reactive interventions into survey designs to prevent or discourage this behavior (Conrad et al., 2011; Zhang, 2013). In this study, we use the 2013 National Survey of College Graduates (NSCG) web paradata to classify respondents based on their tendency to satisfice. Specifically, we use factors such as response time, non-differentiation among answers, omissions, and early termination to identify degrees of satisficing. We also look for correlations between satisficing behaviors and demographic characteristics to help characterize these respondents. Successful classification is instrumental in targeting interventions planned for future studies based on the type of satisficing. Conrad, F. G., Tourangeau, R., Couper,M. P., & Zhang, C. (2011). Interactive Interventions in Web Surveys Can Increase Response Accuracy.
Access/Direct link Conference Homepage (abstract)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - 2016 (264)
- Mixing Modes: Challenges (and Tradeoffs) of Adapting a Mailed Paper Survey to the Web ; 2016; Wilkinson-Flicker, S.; McPhee, C. B.; Medway, R.; Kaiser, A.; Cutts, K.
- An Examination of How Survey Mode Affect Eligibility, Response and Health Condition Reporting Rates...; 2016; Stern, M. J.; Ghandour, R.
- Investigating Measurement Error through Survey Question Placement ; 2016; Wilson, A.; Wine, J.; Janson, N.; Conzelmann, J.; Peytcheva, E.
- Instructions in Self-administered Survey Questions: Do They Improve Data Quality or Just Make the Questionnaire...; 2016; Redline, C. D.; Zukerberg, A.; Owens, C.; Ho, A.
- Usability Testing within Agile Process; 2016; Holland, T.
- Exploring Why Web Surveys Take Longer to Complete on Smartphones than PCs: Findings from a Within-subjects...; 2016; Antoun, C.; Cernat, A.
- Making Mobile Web Surveys Accessible; 2016; Malakhoff, L.
- Association of Eye Tracking with Other Usability Metrics ; 2016; Olmsted, E. L.
- Cognitive Probing Methods in Usability Testing – Pros and Cons; 2016; Nichols, E. M.
- Grids and Online Surveys: Do More Complex Grids Induce Survey Satisficing? Evidence from the Gallup...; 2016; Wang, Me.; McCutcheon, A. L.
- Assessing the Accuracy of 51 Nonprobability Online Panels and River Samples: A Study of the Advertising...; 2016; Yang,Y.;Callegaro,M.;Yang,Y.;Callegaro,M.;Chin,K.;Yang,Y.;Villar,A.;Callegaro, M.; Chin, K.; Krosnick...
- Calculating Standard Errors for Nonprobability Samples when Matching to Probability Samples ; 2016; Lee, Ad.; ZuWallack, R. S.
- Communicating Data Use and Privacy: In-person versus Web based methods for message testing ; 2016; Clark Fobia, A.; Hunter Childs, J. E.
- User Experience and Eye-tracking: Results to Optimize Completion of a Web Survey and Website Design ; 2016; Walton, L.; Ricci, K.; Libman Barry, A.; Eiginger, C.; Christian, L. M.
- Estimated-control Calibrated Estimates from Nonprobability Surveys; 2016; Dever, J. A.
- Decomposing Selection Effects in Non-probability Samples ; 2016; Mercer, A. W.; Keeter, S.; Kreuter, F.
- The Effect of Emphasizing the Web Option in a Mixed-mode Establishment Survey ; 2016; O'Brien, J.; Rajapaksa, S.; Schafer, B.; Langetieg, P.
- A Multi-phase Exploration Into Web-based Panel Respondents: Assessing Differences in Recruitment, Respondents...; 2016; Redlawsk, D.; Rogers, K.; Borie-Holtz, D.
- Effect of Clarifying Instructions on Response to Numerical Open-ended Questions in Self-administered...; 2016; Kumar Chaudhary, A.; Israel, G. D.
- Exploring the Feasibility of Using Facebook for Surveying Special Interest Populations ; 2016; Lee, C.; Jang, S.
- National Estimates of Sexual Minority Women Alcohol Use through Web Based Respondent Driven Sampling...; 2016; Farrell Middleton, D.; Iachan, R.; Freedner-Maguire, N.; Trocki, K.; Evans, C.
- Bringing Fair Market Rent Surveys into the 21st Century – Evaluating the Effectiveness of MSG...; 2016; Dayton, J.; Brassell, T.; Cooper, V.; Dion, R.; Williams, R.
- Measuring Survey Behavior of Smartphone Users; 2016; Luks, S.; Phillips, R.
- Practical Considerations for Using Vignettes to Evaluate Survey Items ; 2016; Steiger, D. M.; Williams, Do.; Edwards, W. S.; Cantor, D.; Truman, J.
- Using Web Panels to Quantify the Qualitative: The National Center for Health Statistics Research and...; 2016; Scanlon, P. J.
- Impact of Field Period Length in the Estimates of Sexual Victimization in a Web-based Survey of College...; 2016; Berzofsky, M.; Peterson, K.; Shook-Sa, B. E.; Lindquist, C.; Krebs, C.
- Longitudinal Online Ego-centric Social Network Data Collection with EgoWeb 2.0 ; 2016; Amin, A.; Kennedy, D.
- Influences on Item Response Times in a Multinational Web Survey ; 2016; Phillips, B. T.; Kolenikov, S.; Howard Ecklund, E.; Ackermann, A.; Brulia, A.
- QR Codes for Survey Access: Is It Worth It?; 2016; Allen, L.; Marlar, J.
- An Exploration of the Relationship between Usability Testing and Data Verification ; 2016; Langer Tesfaye, C.; Kurmlavage, V.
- Beyond the Survey: Improving Data Insights and User Experience with Mobile Devices ; 2016; Graham, P.; Lew, G.
- User Experience Considerations for Contextual Product Surveys on Smartphones ; 2016; Sedley, A.; Mueller, H.
- The Differential Effect of Mobile-friendly Surveys on Data Quality; 2016; Horwitz, R.
- Embedding Survey Questions within Non-research Mobile Apps: A Method for Collecting High-quality Data...; 2016; Bapna, V.; Antoun, C.
- Does Changing Monetary Incentive Schemes in Panel Studies Affect Cooperation? A Quasi-experiment on...; 2016; Schaurer, I.; Bosnjak, M.
- Survey Mode and Mail Method: A Practical Experiment in Survey Fielding for a Multi-round Survey ; 2016; Sullivan, B. D.; Duda, N.; Bogen, K.; Clusen, N. A.; Wakar, B.; Zhou, H.
- Web Probing for Question Evaluation: The Effects of Probe Placement ; 2016; Fowler, S.; Willis, G. B.; Moser, R. P.; Townsend, R. L. M.; Maitland, A.; Sun, H.; Berrigan, D.
- Early-bird Incentives: Results From an Experiment to Determine Response Rate and Cost Effects ; 2016; De Santis, J.; Callahan, R.; Marsh, S.; Perez-Johnson, I.
- Using Cash Incentives to Help Recruitment in a Probability Based Web Panel: The Effects on Sign Up Rates...; 2016; Krieger, U.
- Assessing Changes in Coverage Bias of Web Surveys a s Internet Access Increases in the United States...; 2016; Sterrett, D.; Malato, D.; Benz, J.; Tompson, T.; English, N.
- Timing is Everything: Discretely Discouraging Mobile Survey Response through the Timing of Email Contacts...; 2016; Richards, A.; C.; Shook-Sa, B. E.; C.; Berzofsky, M.; Smith, A. C.
- Dynamic Instructions in Check-All-That-Apply Questions ; 2016; Kunz, T.; Fuchs, M.
- Patterns of Unit and Item Nonresponse in a Multinational Web Survey ; 2016; Ackermann, A.; Howard Ecklund, E.; Phillips, B. T.; Brulia, A.
- Debunking Myths About the Quality of Industry and O ccupation Data Collected Through Self-administered...; 2016; Hurwitz, F. I.; Stein, J.; Skaff, A. L.
- Desktops, Tablets and Phones, Oh My! Device Prefere nce for Web Based Surveys ; 2016; Schy, S.; Ghirardelli, A.; Morrison, H.
- Assessing Potential Bias in Respondent-driven Incident Based Data from a Web Survey of College Students...; 2016; Peterson, K.; Berzofsky, M.; Shook-Sa, B. E.; Krebs, C.; Lindquist, C.
- Making Connections on the Internet: Online Survey Panel Communications ; 2016; Libman Barry, A.; Eiginger, C.; Walton, L.; Ricci, K.
- The Best of Both Worlds: Utilizing Best Practices From Web and Survey Design ; 2016; Libman Barry, A.; Langer Tesfaye, C.; Levy, J.
- Characterizing Satisficers in Web Surveys Using Paradata to Target Interventions; 2016; Vetting, S. S.; Horwitz, R.; Bray, R.; Hernandez Vivier, A.; Tancreto, J.; Reiser, C.
- A Closer Look at Response Time Outliers in Online S urveys Using Paradata Survey Focus ; 2016; Schlosser, S.; Hoehne, J. K.