Web Survey Bibliography
Title Adjusting measurement bias in sequential mixed-mode surveys using re-interview data
Source Statistics Netherlands, Discussion Paper, 2015/23
Year 2015
Access date 17.05.2016
Abstract In mixed-mode surveys, mode-differences in measurement bias, also called measurement effects or mode effects, continue to pose a problem to survey
practitioners. In this paper, we discuss statistical adjustment of measurement bias to the level of a measurement benchmark mode during inference from mixed-mode data. In doing so, statistical methodology requires auxiliary information which we suggest to collect in a re-interview administered to a sub-set of respondents to the first stage of a sequential mixed-mode survey. In the re-interview, relevant questions from the main survey are repeated. After introducing the design and presenting relevant statistical theory, this paper evaluates the performance of a set of six candidate estimators that exploit re-interview information in a Monto Carlo simulation. In the simulation, a large number of parameters is systematically varied, which define the size and type of measurement and selection effects between modes in the mixed-mode design. Our results indicate that the performance of the estimators strongly depends on the true measurement error model. However, one estimator, called the inverse regression estimator, performs particularly well under all considered scenarios. Our results suggest that the re-interview method is a useful approach to adjust measurement effects in the presence of non-ignorable selectivity between modes in mixed-mode data.
practitioners. In this paper, we discuss statistical adjustment of measurement bias to the level of a measurement benchmark mode during inference from mixed-mode data. In doing so, statistical methodology requires auxiliary information which we suggest to collect in a re-interview administered to a sub-set of respondents to the first stage of a sequential mixed-mode survey. In the re-interview, relevant questions from the main survey are repeated. After introducing the design and presenting relevant statistical theory, this paper evaluates the performance of a set of six candidate estimators that exploit re-interview information in a Monto Carlo simulation. In the simulation, a large number of parameters is systematically varied, which define the size and type of measurement and selection effects between modes in the mixed-mode design. Our results indicate that the performance of the estimators strongly depends on the true measurement error model. However, one estimator, called the inverse regression estimator, performs particularly well under all considered scenarios. Our results suggest that the re-interview method is a useful approach to adjust measurement effects in the presence of non-ignorable selectivity between modes in mixed-mode data.
Access/Direct link Statistics Netherlands Homepage
Year of publication2015
Bibliographic typeReports, seminars
Web survey bibliography - Reports, seminars (231)
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2016; 2016
- Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys; 2016
- FocusVision 2015 Annual MR Technology Report; 2016; Macer, T., Wilson, S.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- Assessment of Innovations in Data Collection Technology for Understanding Society; 2016; 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...
- Evaluating a New Proposal for Detecting Data Falsification in Surveys; 2016; Simmons, K.; Mercer, A. W.; Schwarzer, S.; Courtney, K.
- Computer-assisted and online data collection in general population surveys; 2016; Skarupova, K.
- Predictive inference for non-probability samples: a simulation study ; 2016; Buelens, B.; Burger, J.; van den Brakel, J.
- ESOMAR/GRBN Online Research Guideline; 2015
- App vs. Web for Surveys of Smartphone Users: Experimenting with mobile apps for signal-contingent experience...; 2015; McGeeney, K.; Keeter, S.; Igielnik, R.; Smith, A.; Rainie, L.
- On Climbing Stairs Many Steps at a Time: The New Normal in Survey Methodology; 2015; Dillman, D. A.
- Polling Error in the 2015 UK General Election: An Analysis of YouGov’s Pre and Post-Election Polls...; 2015; Wells, A.; Rivers, D.
- GreenBook Research Industry Trends Report; 2015; Murphy, L. (Ed.)
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2015; 2015
- Methodology of the RAND Mid-Term 2014 Election Panel; 2015; Carman, K. G; Pollack, S.
- 28 Questions to Help Buyers of Online Samples; 2015; Cape, P. J.; Phillips, A.; Baker, R.; Cooke, M.; Ribeiro, E.; Terhanian, G.
- Understanding Society Innovation Panel Wave 7: Results from Methodological Experiments; 2015; Blom, A. G.; Burton, J.; Booker, C. L.; Cernat, A.; Fairbrother, M.; Jaeckle, A.; Kaminska, O.; Keusch...
- Tips for Creating Web Surveys for Completion on a Mobile Device; 2015; McGeeney, K.
- U.S. Survey Research: Sampling; 2015
- A Comparison of Different Online Sampling Approaches for Generating National Samples; 2014; Heen, M. S. J., Lieberman, J. D., Miethe, T. D.
- FocusVision 2014 Annual MR Technology Report; 2014; Macer, T., Wilson, S.
- The Changing Landscape of Technology and its Effect on Online Survey Data Collection; 2014; Mitchell, N.
- Query on Data Collection for Social Surveys; 2014; Blanke, K., Luiten, A.
- The role of email addresses and email contact in encouraging web response in a mixed mode design ; 2014; Cernat, A., Lynn, P.
- Mixed-mode surveys of the general population - Results from the European Social Survey mixed-mode experiment...; 2014; Park, A., Humphrey, A.
- Mixed-Mode Designs bei Erhebungen mit sensitiven Fragen: Einfluss auf das Teilnahme- und Antwortverhalten...; 2014; Krug, G., Kriwy, P., Carstensen, J.
- Methods and systems for managing an online opinion survey service; 2014; Mcloughlin, M. H., Seton, N., Blesy, K.
- Mobile Technologies for Conducting, Augmenting and Potentially Replacing Surveys: Report of the AAPOR...; 2014; Link, M. W., Murphy, J., Schober, M. F., Buskirk, T. D., Childs, J. H., Tesfaye, C.
- The use of within-subject experiments for estimating measurement effects in mixed-mode surveys ; 2014; Klausch, L. T., Schouten, B., Hox, J.
- Measuring well-being: An analysis of different response scales; 2014; van Beuningen, J., van der Houwen, K., Moonen, L.
- The impact of contact effort and interviewer performance on mode-specific nonresponse and measurement...; 2014; Schouten, B., Cobben, F., van der Laan, J., Arends, J.
- Community Life Survey: Summary of web experiment findings; 2013
- The Short-term Campaign Panel of the German Longitudinal Election Study 2009. Design, Implementation...; 2013; Steinbrecher, M., Rossmann, J.
- Too Fast, Too Straight, Too Weird: Post Hoc Identification of Meaningless Data in Internet ; 2013; Leiner, D. J.
- Postal recruitment into a longitudinal online panel survey. The effects of different number of reminder...; 2013; Martinsson, J.
- The world in 2013. ICT facts and figures; 2013
- Microsoft Security Intelligence Report, Volume 15; 2013
- A Comparison of Results from a Spanish and English Mail Survey: Effects of Instruction Placement on...; 2013; Wang, K., Sha, M.
- Research Note: Reducing the Threat of Sensitive Questions in Online Surveys?; 2013; Couper, M. P.
- Global market research 2013; 2013
- Exploring the Digital Nation: America’s Emerging Online Experience; 2013
- Advantages of a global multimodal print & digital readership survey; 2013; Cour, N., Saint-Joanis, G.
- Australia: building a 21st century readership survey; 2013; Green, A., White, H.
- The new swiss national readership survey: fit for the future ; 2013; Amschler, H., Hoffmann, J.
- ESS Mixed Mode Experiment Results in Estonia (CAWI and CAPI Mode Sequential Design); 2013; Ainsaar, M., Lilleoja, L., Lumiste, K., Roots, A.
- Using smartphones in survey research: a multifunctional tool Implementation of a time use app; a feasability...; 2013; Sonck, N., Fernee, H.
- Adaptive survey designs to minimize survey mode effects. A case study on the Dutch Labour Force Survey...; 2013; Calinescu, M., Schouten, B.
- Optimal Resource Allocation in Adaptive Survey Designs; 2013; Calinescu, M.