Web Survey Bibliography
Concern about data falsification is as old as the profession of public opinion polling. However, the extent of data falsification is difficult to quantify and not well documented. As a result, the impact of falsification on statistical estimates is essentially unknown. Nonetheless, there is an established approach to address the problem of data falsification which includes prevention, for example by training interviewers and providing close supervision, and detection, such as through careful evaluation of patterns in the technical data, also referred to as paradata, and the substantive data.
In a recent paper, Kuriakose and Robbins (2015) propose a new approach to detecting falsification. The measure is an extension of the traditional method of looking for duplicates within datasets. What is new about their approach is the assertion that the presence of respondents that match another respondent on more than 85% of questions, what we refer to as a high match, indicates likely falsification. They apply this threshold to a range of publicly available international survey datasets and conclude that one-in-five international survey datasets likely contain falsified data.
The claim that there is widespread falsification in international surveys is clearly concerning. However, an extensive investigation conducted by Pew Research Center and summarized in this report finds the claim is not well supported. The results demonstrate that natural, benign survey features can explain high match rates. Specifically, the threshold that Kuriakose and Robbins propose is extremely sensitive to the number of questions, number of response options, number of respondents, and homogeneity within the population. Because of this sensitivity to multiple parameters, under real-world conditions it is possible for respondents to match on any percentage of questions even when the survey data is valid and uncorrupted. In other words, our analysis indicates the proposed threshold is prone to generating false positives – suggesting falsification when, in fact, there is none. Perhaps the most compelling evidence that casts doubt on the claim of widespread falsification is in the way the approach implicates some high-quality U.S. surveys. The threshold generates false positives in data with no suspected falsification but that has similar characteristics to the international surveys called into question.
This paper proceeds as follows. First, we briefly review the problem of data falsification in surveys and how it is typically addressed. Second, we summarize Kuriakose and Robbins’ argument for their proposed threshold for identifying falsified data and discuss our concerns about their evidence. Third, we outline the research steps we followed to evaluate the proposed threshold and then review in detail the results of our analysis. Finally, we conclude with a discussion of the findings and other ways the field is working to improve quality control methods.
Web survey bibliography - 2016 (264)
- Privacy Concerns in Responses to Sensitive Questions. A Survey Experiment on the Influence of Numeric...; 2016; Bader, F., Bauer, J., Kroher, M., Riordan, P.
- Ballpoint Pens as Incentives with Mail Questionnaires – Results of a Survey Experiment; 2016; Heise, M.
- Non-Observation Bias in an Address-Register-Based CATI/CAPI Mixed Mode Survey; 2016; Lipps, O.
- Spatial Modeling through GIS to Reveal Error Potent ial in Survey Data: Where, What and How Much ; 2016; English, N.; Ventura, I.; Bilgen, I.; Stern, M. J.
- Bees to Honey or Flies to Manure? How the Usual Subject Recruitment Exacerbates the Shortcomings of...; 2016; Snell, S. A., Hillygus, D. S.
- Thinking Inside the Box Visual Design of the Response Box Affects Creative Divergent Thinking in an...; 2016; Mohr, A. H.; Sell, A.; Lindsay, T.
- Detecting careless respondents in web-based questionnaires: Which method to use?; 2016; Niesen, A. S. M.; Meijer, R. R.; Tendeiro, J. N.
- Web surveys for offline rural communities ; 2016; Gichohi, B. W.
- On-line life history calendar and sensitive topics: A pilot study; 2016; Morselli, D.; Berchtold, A.; Granell, J.-C. S.; Berchtold, And.
- Does survey mode matter for studying electoral behaviour? Evidence from the 2009 German Longitudinal...; 2016; Bytzek, E.; Bieber, I. E.
- The impact of visual design and response formats on data quality in a web survey of MOOC students; 2016; Maloshonok, N.; Terentev, E.
- An experiment comparing grids and item-by-item formats in web surveys completed through PCs and smartphones...; 2016; Revilla, M.; Toninelli, D.; Ochoa, C.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Gamifying Questions Using Text Alone; 2016; Cape, P. J.
- Assessing the Effects of Participant Preference and Demographics in the Usage of Web-based Survey Questionnaires...; 2016; Mlikotic, R.; Parker, B.; Rajapakshe, R.
- Improving Inpatient Surveys: Web-Based Computer Adaptive Testing Accessed via Mobile Phone QR Codes; 2016; Chien, T. S.; Lin, W.S.
- Surveying End-of-Life Medical Decisions in France: Evaluation of an Innovative Mixed-Mode Data Collection...; 2016; Legleye, S; Pennec, S.; Monnier, A.; Stephan, A.; Brouard, N.; Bilsen, J.; Cohen, J.
- Problems and Prospects in Survey Research; 2016; Moy, P.; Murphy, J.
- Eye-tracking Social Desirability Bias; 2016; Kaminska, O.; Foulsham, T.
- Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey:...; 2016; Dal Grande, E.; Chittleborough, C. R.; Campostrini, S.; Dollard, M.; Taylor, A. W.
- Pitfalls, Potentials, and Ethics of Online Survey Research: LGBTQ and Other Marginalized and Hard-to...; 2016; McInroy, L. B.
- Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates; 2016; Trespalacios, J. H.; Perkins, R. A.
- Forecasting proportional representation elections from non-representative expectation surveys; 2016; Graefe, A.
- Short and Sweet? Length and Informative Content of Open-Ended Responses Using SMS as a Research Mode; 2016; Walsh, E.; Brinker, J. K.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- Comparing online and telephone survey results in the context of a skin cancer prevention campaign evaluation...; 2016; Hollier, L.P.; Pettigrew, S.; Slevin, T.; Strickland, M.; Minto, C.
- Options for Fielding and Analyzing Web Surveys; 2016; Schonlau, M.; Couper, M. P.
- Report of the Inquiry into the 2015 British general election opinion polls; 2016; Sturgis, P., Baker, N., Callegaro, M., Fisher, St., Green, J., Jennings, W., Kuha, J., Lauderdale, B...
- Sample Representation and Substantive Outcomes Using Web With and Without Incentives Compared to Telephone...; 2016; Lipps, O.; Pekari, N.
- Effects of Data Collection Mode and Response Entry Device on Survey Response Quality; 2016; Ha, L.; Zhang, Che.; Jiang, W.
- The Dynamic Identity Fusion Index: A New Continuous Measure of Identity Fusion for Web-Based Questionnaires...; 2016; Jimenez, J.; Gomez, A.; Buhrmester, M.; Whitehouse, H.; Swann, W. B.
- Navigation Buttons in Web-Based Surveys: Respondents’ Preferences Revisited in the Laboratory; 2016; Romano Bergstrom, J. C.; Erdman, C.; Lakhe, S.
- Collecting Data from mHealth Users via SMS Surveys: A Case Study in Kenya; 2016; Johnson, D.
- “Money Will Solve the Problem”: Testing the Effectiveness of Conditional Incentives for...; 2016; DeCamp, W.; Manierre, M. J.
- Effects of Incentive Amount and Type of Web Survey Response Rates; 2016; Coopersmith, J.; Vogel, L. K.; Bruursema, T.; Feeney, K.
- Effect of a Post-paid Incentive on Response to a Web-based Survey; 2016; Brown, J. A.; Serrato, C. A.; Hugh, M.; Kanter, M. H.; A.; Spritzer, K. L.; Hays, R. D.
- Dropouts in Longitudinal Surveys; 2016; Lugtig, P. J.; De Leeuw, E. D.
- Participant recruitment and data collection through Facebook: the role of personality factors; 2016; Rife, S. C.; Cate, K. L.; Kosinski, M.; Stillwell, D.
- Evaluating a New Proposal for Detecting Data Falsification in Surveys; 2016; Simmons, K.; Mercer, A. W.; Schwarzer, S.; Courtney, K.
- Quantifying Under- and Overreporting in Surveys Through a Dual-Questioning-Technique Design. ; 2016; de Jong , M.; Fox, J.-P.; Steenkamp, J. - B. E. M.
- Take the money and run? Redemption of a gift card incentive in a clinician survey. ; 2016; Chen, J. S.; Sprague, B. L.; Klabunde, C. N.; Tosteson, A. N. A.; Bitton, A.; Onega, T.; MacLean, C....
- Solving the Nonresponse Problem With Sample Matching?; 2016
- Do Polls Still Work If People Don't Answer Their Phones?; 2016; Edwards-Levy, A.; Jackson, N. M.
- A Framework of Incorporating Thai Social Networking Data in Online Marketing Survey; 2016; Jiamthapthaksin, R.; Aung, T. H.; Ratanasawadwat, N.
- Development of a scale to measure skepticism toward electronic word-of-mouth; 2016; Zhang, Xia.; Ko, M.; Carpenter, D.
- Improving social media measurement in surveys: Avoiding acquiescence bias in Facebook research; 2016; Kuru, O.; Pasek, J.
- Psychological research in the internet age: The quality of web-based data; 2016; Ramsey, S. R.; Thompson, K. L.; McKenzie, M.; Rosenbaum, A.
- Internet Abusive Use Questionnaire: Psychometric properties; 2016; Calvo-Frances, F.
- The impact of academic sponsorship on Web survey dropout and item non-response; 2016; Allen, P. J.; Roberts, L. D.
- Measuring Generalized Trust: An Examination of Question Wording and the Number of Scale Points; 2016; Lundmark, S.; Giljam, M.; Dahlberg, S.