Web Survey Bibliography
Title Total Survey Error in Practice: Improving Quality in the Era of Big Data
Author Biemer, P. P.; De Leeuw, E. D.; Eckman, S.; Edwards, B.; Kreuter, F.; Lyberg, L. E.; Tucker, C.; West, B. T.; (eds.)
Source Wiley
Year 2016
Database Willey InterScience
Access date 18.08.2016
Abstract
An edited volume for an upcoming conference on Total Survey Error (TSE), this book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation and analysis. The book recognizes that survey data affects many public policy and business decisions, and thus focuses on the framework for understanding and improving survey data quality.
The book also addresses issues with data quality in official statistics and in survey, opinion, and market research as the field of statistics has changed, leading to larger and messier data sets. This challenges survey organizations to find ways to collect data more efficiently without sacrificing quality.
This volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The TSE Framework; Implications for Survey Design; Data Collection and Data Processing Applications; Evaluation and Improvement; Estimation and Analysis. Each chapter introduces and examines at least one error source, such as sampling error, measurement error, and nonresponse error, which are the most recognized. The TSE framework presented also encourages readers not to lose sight of the less-commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with more total bias.
Examples are provided of recent scandals involving incorrect or misleading official statistics and survey estimates, such as the ongoing controversy over the number of civilians killed in the Iraq war, and how many reputable polling firms made incorrect predictions about the outcome of the 2012 US election. In Sweden, a faulty calculation of the Consumer Price Index led to overpayments of social security benefits and some organizations collecting data in international surveys have been accused of fabricating portions of their data sets. This practical insight on survey data quality presents concerns about the data errors and the methods and approaches necessary to prevent or remove them.
The book also addresses issues with data quality in official statistics and in survey, opinion, and market research as the field of statistics has changed, leading to larger and messier data sets. This challenges survey organizations to find ways to collect data more efficiently without sacrificing quality.
This volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The TSE Framework; Implications for Survey Design; Data Collection and Data Processing Applications; Evaluation and Improvement; Estimation and Analysis. Each chapter introduces and examines at least one error source, such as sampling error, measurement error, and nonresponse error, which are the most recognized. The TSE framework presented also encourages readers not to lose sight of the less-commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with more total bias.
Examples are provided of recent scandals involving incorrect or misleading official statistics and survey estimates, such as the ongoing controversy over the number of civilians killed in the Iraq war, and how many reputable polling firms made incorrect predictions about the outcome of the 2012 US election. In Sweden, a faulty calculation of the Consumer Price Index led to overpayments of social security benefits and some organizations collecting data in international surveys have been accused of fabricating portions of their data sets. This practical insight on survey data quality presents concerns about the data errors and the methods and approaches necessary to prevent or remove them.
Access/Direct link Wiley Homepage (abstract) / (full tex)
Chapters
Chapters
AvailabilityIn-press, to be published
Year of publication2016
Bibliographic typeEdited book
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.