Web Survey Bibliography
Title A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys
Author Geisen, E., Murphy, J.
Year 2017
Access date 10.09.2017
Abstract Due to low costs, improvements in coverage, and technological advances many surveys are now being conducted in whole or in part via self-administered web questionnaires. Increasingly, respondents are choosing to complete web surveys on touch-screen mobile devices such as tablets and smartphones. Recent estimates show that the proportion of respondents completing a survey on a mobile device can be 30% or more for some surveys (Lugtig, Toepoel, and Amin, 2016; Saunders, 2015). Mobile apps are also being used by survey respondents who are panel members and by interviewers to administer household screening surveys. Because of these technological advances, the ways that respondents and interviewers interact with surveys are changing.
With the pace of change in survey administration, we need to consider whether traditional pretesting methodologies address the types of potential quality concerns these newer modes introduce. For example, modern web surveys support dynamic survey features such as hover-over definitions, calculate total buttons, videos/images, error messages, dynamic look-ups, touch-screen, swiping to navigate, GPS, and other capabilities. Each of these features changes the respondent-survey interaction, which can affect the quality of the data collected in a survey.
The purpose of this paper is to introduce emerging survey pretesting methodologies and compare these with traditional methods in the light of modern data collection technologies to consider where the standard approaches for pretesting can be improved. We begin by discussing the key limitations of traditional pretesting methods such as expert review, cognitive interviewing, and pilot testing for evaluating “modern” surveys. We then provide an overview of emerging pretesting methods including usability testing, eye tracking, and crowdsourcing. We discuss the advantages offered by these methods – particularly in terms of budget and schedule—and provide empirical examples of how these methods can improve data quality. We conclude with a theoretical mode for the optimal combination of traditional and newer methods for pretesting modern surveys.
With the pace of change in survey administration, we need to consider whether traditional pretesting methodologies address the types of potential quality concerns these newer modes introduce. For example, modern web surveys support dynamic survey features such as hover-over definitions, calculate total buttons, videos/images, error messages, dynamic look-ups, touch-screen, swiping to navigate, GPS, and other capabilities. Each of these features changes the respondent-survey interaction, which can affect the quality of the data collected in a survey.
The purpose of this paper is to introduce emerging survey pretesting methodologies and compare these with traditional methods in the light of modern data collection technologies to consider where the standard approaches for pretesting can be improved. We begin by discussing the key limitations of traditional pretesting methods such as expert review, cognitive interviewing, and pilot testing for evaluating “modern” surveys. We then provide an overview of emerging pretesting methods including usability testing, eye tracking, and crowdsourcing. We discuss the advantages offered by these methods – particularly in terms of budget and schedule—and provide empirical examples of how these methods can improve data quality. We conclude with a theoretical mode for the optimal combination of traditional and newer methods for pretesting modern surveys.
Access/Direct link Conference Homepage (abstract) / (presentation)
Year of publication2017
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - Noncoverage & sampling (851)
- Using experts’ consensus (the Delphi method) to evaluate weighting techniques in web surveys not...; 2017; Toepoel, V.; Emerson, H.
- Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance...; 2017; Wetzlehuetter, D.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- A test of sample matching using a pseudo-web sample; 2017; Chatrchi, G., Gambino, J.
- A Partially Successful Attempt to Integrate a Web-Recruited Cohort into an Address-Based Sample; 2017; Kott, P. S., Farrelly, M., Kamyab, K.
- Nonprobability sampling as model construction; 2017; Mercer, A. W.
- Enhancing survey participation: Facebook advertisements for recruitment in educational research; 2017; Forgasz, H.; Tan, H.; Leder, G.; McLeod, A.
- Determinants of polling accuracy: the effect of opt-in Internet surveys; 2017; Sohlberg, J.; Gilljam, M.; Martinsson, J.
- Article Establishing an Open Probability-Based Mixed-Mode Panel of the General Population in Germany...; 2017; Bosnjak, M.; Dannwolf, T.; Enderle, T.; Schaurer, I.; Struminskaya, B.; Tanner, A.; Weyandt, K.
- PC, phone or tablet? Use, preference and completion rates for web surveys ; 2017; Brosnan, K.; Gruen, B.; Dolnicar, S.
- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
- Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys; 2016
- Integration of a phone-based household travel survey and a web-based student travel survey; 2016; Verreault, H.; Morency, C.
- Du kommst hier nicht rein: Türsteherfragen identifizieren nachlässige Teilnehmer in Online-Umfragen; 2016; Merkle, B.; Kaczmirek, L.; Hellwig, O.
- Estimation and Adjustment of Self-Selection Bias in Volunteer Panel Web Surveys ; 2016; Niu, Ch.
- Geht’s auch mit der Maus? – Eine Methodenstudie zu Online-Befragungen in der Jugendforschung...; 2016; Heim, R.; Konowalczyk, S.; Grgic, M.; Seyda, M.; Burrmann, U.; Rauschenbach, T.
- FocusVision 2015 Annual MR Technology Report; 2016; Macer, T., Wilson, S.
- Can Student Populations in Developing Countries Be Reached by Online Surveys? The Case of the National...; 2016; Langer, A., Meuleman, B., Oshodi, A.-G. T., Schroyens, M.
- Comparisons of Online Recruitment Strategies for Convenience Samples: Craigslist, Google AdWords, Facebook...; 2016; Antoun, C., Zhang, C., Conrad, F. G., Schober, M. F.
- Comparing Cognitive Interviewing and Online Probing: Do They Find Similar Results?; 2016; Meitinger, K., Behr, D.
- Feature phones no barrier to conducting an effective conjoint study ; 2016; de Rooij, R.; Dossin, R.
- Patient preference: a comparison of electronic patient-completed questionnaires with paper among cancer...; 2016; Martin, P.; Brown, M.C.; Espin‐Garcia, O.; Cuffe, S.; Pringle, D.; Mahler, M.; Villeneuve, J.;...
- Device use in web surveys: The effect of differential incentives; 2016; Mavletova, A. M.; Couper, M. P.
- A look into the challenges of mixed-mode surveys; 2016; Klausch, L. T.
- The use of online social networks as a promotional tool for self-administered internet surveys; 2016; de Rada, V. D.; Arino, L. V. C; Blasco, M. G
- 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...
- 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.
- Non-Observation Bias in an Address-Register-Based CATI/CAPI Mixed Mode Survey; 2016; Lipps, O.
- Bees to Honey or Flies to Manure? How the Usual Subject Recruitment Exacerbates the Shortcomings of...; 2016; Snell, S. A., Hillygus, D. S.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- When will Nonprobability Surveys Mirror Probability Surveys? Considering Types of Inference and Weighting...; 2016; Pasek, J.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- What is the gain in a probability-based online panel to provide Internet access to sampling units that...; 2016; Revilla, M.; Cornilleau, A.; Cousteaux, A-S.; Legleye, S; de Pedraza, P.
- Representative web-survey!; 2016; Linde, P.
- Assessing targeted approach letters: effects in different modes on response rates, response speed and...; 2016; Lynn, P.
- The Analysis of Respondent’s Behavior toward Edit Messages in a Web Survey; 2016; Park, Y.
- The Utility of an Online Convenience Panel for Reaching Rare and Dispersed Populations; 2016; Sell, R.; Goldberg, S.; Conron, K.
- Setting Up an Online Panel Representative of the General Population The German Internet Panel; 2016; Blom, A. G.; Gathmann, C.; Krieger, U.
- Implementation of Web-Based Respondent Driven Sampling among Men Who Have Sex with Men in Sweden; 2016; Stroemdahl, S.; Lu, X.; Bengtsson, L.; Liljeros, F.; Thorson, A.
- 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...
- Participant recruitment and data collection through Facebook: the role of personality factors; 2016; Rife, S. C.; Cate, K. L.; Kosinski, M.; Stillwell, D.
- Online Surveys are Mixed-Device Surveys. Issues Associated with the Use of Different (Mobile) Devices...; 2016; Toepoel, V.; Lugtig, P. J.
- Electronic and paper based data collection methods in library and information science research: A comparative...; 2016; Tella, A.
- The Validity of Surveys: Online and Offline; 2016; Wiersma, W.
- Computer-assisted and online data collection in general population surveys; 2016; Skarupova, K.
- Sunday shopping – The case of three surveys; 2016; Bethlehem, J.