Web Survey Bibliography
Title Assessing the Accuracy of 51 Nonprobability Online Panels and River Samples: A Study of the Advertising Research Foundation 2013 Online Panel Comparison Experiment
Author Yang,Y.;Callegaro,M.;Yang,Y.;Callegaro,M.;Chin,K.;Yang,Y.;Villar,A.;Callegaro, M.; Chin, K.; Krosnick, J. A.; Villar, A.; Yang, Y.
Year 2016
Access date 09.06.2016
Abstract
Survey research is increasingly conducted using online panels and river samples. With a large number of data suppliers available, data purchasers need to understand the accuracy of the data being provided and whether probability sampling continues to yield more accurate measurements of populations. This paper evaluates the accuracy of a probability sample and non-probability survey samples that were created using various different quota sampling strategies and sample sources (panel versus river
samples) on the accuracy of estimates. Data collection was organized by the Advertising Research Foundation (ARF) in 2013. We compare estimates from 45 U.S. online panels of non-probability samples, 6 river samples, and one RDD telephone sample to high-quality benchmarks -- population estimates obtained from large-scale face-to-face surveys of probability samples with extremely high response rates (e.g., ACS, NHIS, and NHANES). The non-probability samples were supplied by 17 major U.S. providers. Online respondents were directed to a third party website where the same questionnaire was administered. The online samples were created using three quota methods: (A) age and gender within regions; (B) Method A plus race/ethnicity; and (C) Method B plus education. Mean questionnaire completion time was 26 minutes, and the average sample size was 1,118. Comparisons are made using unweighted and weighted data, with different weighting strategies of increasing complexity. Accuracy is evaluated using the absolute average error method, where the percentage of respondents who chose the modal category in the benchmark survey is compared to the corresponding percentage in each sample. The study illustrates the need for methodol
ogical rigor when evaluating the performance of survey samples.
samples) on the accuracy of estimates. Data collection was organized by the Advertising Research Foundation (ARF) in 2013. We compare estimates from 45 U.S. online panels of non-probability samples, 6 river samples, and one RDD telephone sample to high-quality benchmarks -- population estimates obtained from large-scale face-to-face surveys of probability samples with extremely high response rates (e.g., ACS, NHIS, and NHANES). The non-probability samples were supplied by 17 major U.S. providers. Online respondents were directed to a third party website where the same questionnaire was administered. The online samples were created using three quota methods: (A) age and gender within regions; (B) Method A plus race/ethnicity; and (C) Method B plus education. Mean questionnaire completion time was 26 minutes, and the average sample size was 1,118. Comparisons are made using unweighted and weighted data, with different weighting strategies of increasing complexity. Accuracy is evaluated using the absolute average error method, where the percentage of respondents who chose the modal category in the benchmark survey is compared to the corresponding percentage in each sample. The study illustrates the need for methodol
ogical rigor when evaluating the performance of survey samples.
Access/Direct link Conference Homepage (abstract)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - Noncoverage & sampling (851)
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- A Partially Successful Attempt to Integrate a Web-Recruited Cohort into an Address-Based Sample; 2017; Kott, P. S., Farrelly, M., Kamyab, K.
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- Determinants of polling accuracy: the effect of opt-in Internet surveys; 2017; Sohlberg, J.; Gilljam, M.; Martinsson, J.
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- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
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- 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.