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 (109)
- Telephone versus Online Survey Modes for Election Studies: Comparing Canadian Public Opinion and Vote...; 2017; Breton, C.; Cutler, F.; Lachance, S.; Mierke-Zatwarnicki, A.
- Comparing acquiescent and extreme response styles in face-to-face and web surveys; 2017; Liu, M.; Conrad, F. G.; Lee, S.
- The Failure of the Polls: Lessons Learned from the 2015 UK Polling Disaster; 2017; Sturgis, P.
- Incorporating eye tracking into cognitive interviewing to pretest survey questions; 2016; Neuert, C.; Lenzner, T.
- Are interviews costing £0.08 a waste of money? Reviewing Google Surveys for Wisdom of the Crowd...; 2016; Roughton, G.; MacKay, I.
- The Effects of a Delayed Incentive on Response Rates, Response Mode, Data Quality, and Sample Bias in...; 2016; McGonagle, K., Freedman, V. A.
- Privacy Concerns in Responses to Sensitive Questions. A Survey Experiment on the Influence of Numeric...; 2016; Bader, F., Bauer, J., Kroher, M., Riordan, P.
- Does survey mode matter for studying electoral behaviour? Evidence from the 2009 German Longitudinal...; 2016; Bytzek, E.; Bieber, I. E.
- Forecasting proportional representation elections from non-representative expectation surveys; 2016; Graefe, A.
- Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk; 2016; Berinsky, A.; Huber, G. A.; Lenz, G. S.
- 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.
- Evaluating a New Proposal for Detecting Data Falsification in Surveys; 2016; Simmons, K.; Mercer, A. W.; Schwarzer, S.; Courtney, K.
- Identifying Pertinent Variables for Nonresponse Follow-Up Surveys. Lessons Learned from 4 Cases in Switzerland...; 2016; Vandenplas, C.; Joye, D.; Staehli, M. E.; Pollien, A.
- Methods can matter: Where Web surveys produce different results than phone interviews; 2016; Keeter, S.
- HUFFPOLLSTER: Why Reaching Latinos Is A Challenge For Pollsters; 2016; Jackson, N. M.; Edwards-Levy, A.; Velencia, J.
- Moderators of Candidate Name-Order Effects in Elections: An Experiment; 2016; Kim, Nu.; Krosnick, J. A.; Casasanto, D.
- Measuring Generalized Trust: An Examination of Question Wording and the Number of Scale Points; 2016; Lundmark, S.; Giljam, M.; Dahlberg, S.
- Online and Social Media Data As an Imperfect Continuous Panel Survey; 2016; Diaz, F.; Garmon, F.; Hofman, J. K.; Kiciman, E.; Rothschild, D.
- Translating Answers to Open-ended Survey Questions in Cross-cultural Research: A Case Study on the Interplay...; 2015; Behr, D.
- Using Video to Reinvigorate the Open Question; 2015; Cape, P.
- On Bias Adjustments for Web Surveys; 2015; Fan, L.; Lou, W.; Landsman, V.
- Measuring Political Knowledge in Web-Based Surveys: An Experimental Validation of Visual Versus Verbal...; 2015; Munzert, S.; Selb, P.
- Mode System Effects in an Online Panel Study: Comparing a Probability-based Online Panel with two Face...; 2015; Struminskaya, B.; De Leeuw, E. D.; Kaczmirek, L.
- Data collection mode effect on feeling thermometer questions: A comparison of face-to-face and Web surveys...; 2015; Liu, M., Wang, Yi.
- Do Attempts to Improve Respondent Attention Increase Social Desirability Bias?; 2015; Clifford, S.; Jerit, J.
- HUFFPOLLSTER: Pollsters Debate If Modern Surveys Can Be Trusted; 2015; Blumenthal, M.; Edwards-Levy, A.; Velencia, J.
- Can a non-probabilistic online panel achieve question quality similar to that of the European Social...; 2015; Revilla, M.; Saris, W. E.; Loewe, G.; Ochoa, C.
- Data Collection Mode Effects On Political Knowledge; 2014; Liu, M., Wang, Y.
- Self-reported cheating in web surveys on political knowledge; 2014; Jensen, C., Thomsen, J. P. F.
- The Power of Partisanship in Brazil: Evidence from Survey Experiments; 2014; Samuels, D., Zucco, C.
- Online Polls and Registration-Based Sampling: A New Method for Pre-Election Polling; 2014; Barber, M. J., Mann, C. B., Monson, J. Q., Patterson, K. D.
- Does Survey Mode Still Matter? Findings from a 2010 Multi-Mode Comparison; 2014; Ansolabehere, S., Schaffner, B. F.
- Measuring Political Participation—Testing Social Desirability Bias in a Web-Survey Experiment; 2014; Persson, M., Solevid, M.
- What Does the Satisfaction with Democracy Measure Mean to Respondents in Different Countries? How Cross...; 2014; Behr, D., Braun, M.
- Professional respondents in nonprobability online panels; 2014; Hillygus, D. S., Jackson, N. M., Young, M.
- Online panels and validity; 2014; Groenlund, K., Strandberg, K.
- 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.
- The Short-term Campaign Panel of the German Longitudinal Election Study 2009. Design, Implementation...; 2013; Steinbrecher, M., Rossmann, J.
- Relative Mode Effects on Data Quality in Mixed-Mode Surveys by an Instrumental Variable; 2013; Vannieuwenhuyze, J. T. A., Revilla, M.
- Web Versus Outbound: A Mode Face-Off Following the Presidential Debate; 2013; Marlar, J.
- Propensity Score Weighting – Can Personality Adjust for Selectivity?; 2013; Glantz, A., Greszki, R.
- Especially for You: Motivating Respondents in an Internet Panel by Offering Tailored Questions; 2012; Oudejans, M.
- Presidential Elections in Iceland 2012 – Did online panel surveys give false hope to new candidates...; 2012; Jonsdottir, G. A., Dofradottir, A. G., Bjornsdottir, A. E.
- Effects of Technical Difficulties on Item Nonresponse and Response Favorability in a Mixed-Mode Survey...; 2012; Gibson, J. L.
- Where is Neutral? Using Negativity Biases to Interpret Thermometer Scores; 2012; Soroka, S., Albaugh, Q.
- I Got a Feeling: Comparison of Feeling Thermometers with Verbally Labeled Scales in Attitude Measurement...; 2012; Thomas, R. K., Bremer, J.
- Scrutinizing Dynamics – Rolling panel waves in theory and practice; 2012; Faas, T., Blumenberg, J. N.
- Toward wiser public judgment; 2011; Yankelovich, D., Friedman, W.
- Mass informed consent: Evidence on upgrading democracy with polls and new media; 2011; Simon, A. F.