Web Survey Bibliography
Title Propensity score weighting in a web-based panel survey: Comparing the effects on attrition biases in attitudinal, behavioral, and socio-demographic variables
Author Gummer, T.; Rossmann, J.
Year 2016
Access date 29.04.2016
Presentation PDF (1.02MB)
Abstract
Relevance & Research Question: Propensity score weighting (PSW) is frequently used to correct for attrition biases in panel surveys. While there is a rich methodological literature on the logic of PSW and studies on its practical application, we face a lack of in-depth discussion on the effects of using PSW to correct for attrition biases in attitudinal, behavioral, and socio-demographic variables. Consequently, we address the questions, first, whether there are differences in attrition biases between different types of variables and, second, whether we can identify patterns in the effects of applying PSW across these types of variables.
Methods & Data: Our analysis draws on data from a seven-wave web-based split-panel survey conducted during the campaign to the 2013 German federal election. The panel is supplemented with cross-sectional surveys that are comparable in terms of sampling and questionnaire. We use these cross-sections to assess attrition biases in the corresponding waves of the panel survey. The propensity score weights are calculated using the predicted propensity of respondents to participate in consecutive panel waves. The estimation of the response propensities draws on the data from the first panel wave. We assess the effect of applying these weights on attrition bias in 48 attitudinal, 38 behavioral, and 27 socio-demographic variables.
Results: Our results show that PSW successfully reduced biases in 72 out of the 113 variables. However, looking at the three types of variables, we find the rate of success to be lowest for behavioral variables compared to socio-demographics and attitudinal variables. Furthermore, the magnitude of the reduction in biases is lower for socio-demographic and behavioral variables compared to attitudinal variables.
Added Value: Our findings suggest –while considering the estimate-specific nature of bias–, first, that biases vary across different types of variables and, second, that the effects of PSW are not homogeneous across these types. Accordingly, we recommend not to restrict evaluations of attrition in a panel survey to a limited set of (socio-demographic) variables, because this may result in an underestimation of the magnitude of biases and an overestimation of the ability of PSW to reduce biases in other (types of) variables.
Methods & Data: Our analysis draws on data from a seven-wave web-based split-panel survey conducted during the campaign to the 2013 German federal election. The panel is supplemented with cross-sectional surveys that are comparable in terms of sampling and questionnaire. We use these cross-sections to assess attrition biases in the corresponding waves of the panel survey. The propensity score weights are calculated using the predicted propensity of respondents to participate in consecutive panel waves. The estimation of the response propensities draws on the data from the first panel wave. We assess the effect of applying these weights on attrition bias in 48 attitudinal, 38 behavioral, and 27 socio-demographic variables.
Results: Our results show that PSW successfully reduced biases in 72 out of the 113 variables. However, looking at the three types of variables, we find the rate of success to be lowest for behavioral variables compared to socio-demographics and attitudinal variables. Furthermore, the magnitude of the reduction in biases is lower for socio-demographic and behavioral variables compared to attitudinal variables.
Added Value: Our findings suggest –while considering the estimate-specific nature of bias–, first, that biases vary across different types of variables and, second, that the effects of PSW are not homogeneous across these types. Accordingly, we recommend not to restrict evaluations of attrition in a panel survey to a limited set of (socio-demographic) variables, because this may result in an underestimation of the magnitude of biases and an overestimation of the ability of PSW to reduce biases in other (types of) variables.
Access/Direct link Conference Homepage (presentation)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - 2016 (264)
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- Socially Desirable Responding in Web-Based Questionnaires: A Meta-Analytic Review of the Candor Hypothesis...; 2016; Gnambs, T.; Kaspar, K.
- Dynamic Question Ordering in Online Surveys; 2016; Early, K.; Mankoff, J.; Fienberg, S. E.
- How to use online surveys to understand human behaviour concerning window opening in terms of building...; 2016; Fabbri, K.
- Impact of satisficing behavior in online surveys on consumer preference and welfare estimates; 2016; Gao, Z.; House, L. A.; Bi, X.
- Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers; 2016; Guillory, J.; Kim, A.; Murphy, J.; Bradfield, B.; Nonnemaker, J.; Hsieh, Y. P.
- Influence of Importance Statements and Box Size on Response Rate and Response Quality of Open-Ended...; 2016; Kumar Chaudhary, A.; Israel, G. D.
- Web based health surveys: Using a Two Step Heckman model to examine their potential for population health...; 2016; Morrissey, K.; Kinderman, P.; Pontin, E.; Tai, S.; Schwannauer, M.
- “Better do not touch” and other superstitions concerning melanoma: the cross-sectional web...; 2016; Gajda, M.; Kamiñska-Winciorek, G.; Wydmañski, J.; Tukiendorf, A.
- Methods for Evaluating Respondent Attrition in Web-Based Surveys; 2016; Hochheimer, C. J.; Sabo, R. T.; Krist, A. H.; Day, T.; Cyrus, J.; Woolf, S. H.
- The Low Response Score (LRS): A Metric to Locate, Predict, and Manage Hard-to-Survey Populations; 2016; Erdman, C.; Bates, N.
- Targeted Appeals for Participation in Letters to Panel Survey Members; 2016; Lynn, P.
- Can we assess representativeness of cross-national surveys using the education variable?; 2016; Ortmanns, V.; Schneider, S.
- Methodological Aspects of Central Left-Right Scale Placement in a Cross-national Perspective; 2016; Scholz, E.; Zuell, C.
- Fieldwork Effort, Response Rate, and the Distribution of Survey Outcomes: A Multilevel Meta-analysis; 2016; Sturgis, P.; Williams, Jo.; Brunton-Smith, I.; Moore, J.
- Mobile-only web survey respondents; 2016; Lugtig, P. J.; Toepoel, V.; Amin, A.
- Comparison of Face-to-Face and Web Surveys on the Topic of Homosexual Rights; 2016; Liu, M.; Wang, Yic.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.
- Web-Based Statistical Sampling and Analysis; 2016; Quinn, A.; Larson, K.
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2016; 2016
- Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention; 2016; Kuhlmann, T.; Reips, U.-D.; Wienert, J.; Lippke, S.
- Development and Pilot Test of a Mobile Application for Field Data Collection; 2016; Chiappetta, L.; Kerr, M. M.
- Statistical Design for Online Experiments Across Desktops, Tablets, Smartphones (and Maybe Wearable...; 2016; Qian, P.; Sadeghi, S.; Arora, N. K.
- A Case Study on the Use of Propensity Score Adjustments with Web Survey Data; 2016; Parsons, V.
- Motivated Misreporting in Web Panels; 2016; Bach, R.; Eckman, S.
- Are Initial Respondents Different from the Nonresponse Follow-Up Cases? A Study of Probability-Based...; 2016; Zeng, W.; Dennis, J. M.
- Using official surveys to reduce bias of estimates from nonrandom samples collected by web surveys; 2016; Beresovsky, V.; Dorfman, A.; Rumcheva, P.
- Predicting and Preventing Break-Offs in Web Surveys; 2016; Mittereder, F.
- A Feasibility Study of Recruiting and Maintaining a Web Panel of People with Disabilities; 2016; Chandler, J.
- Exploration of Methods for Blending Unconventional Samples with Traditional Probability Samples; 2016; Gellar, J.; Zhou, H.; D.; Sinclair, M. D.
- Ratio of Vector Lengths as an Indicator of Sample Representativeness ; 2016; Shin, H. C.
- Design of Sample Surveys That Complement Observational Data to Achieve Population Coverage; 2016; Slud, E.; Ashmead, R.
- Inferences from Internet Panel Studies and Comparisons with Probability Samples; 2016; Lachan, R.; Boyle, J.; Harding, R.
- Exploring the Gig Economy Using a Web-Based Survey: Measuring the Online 'and' Offline Side...; 2016; Robles, B. J.; McGee, M.
- Comparing data quality between online panel and intercept samples; 2016; Liu, M.
- Effect of a Pre-Paid Incentive on Response Rates to an Address-Based Sampling (ABS) Web-Mail Survey; 2016; Suzer-Gurtekin, Z.; Elkasabi, M.; Liu, Me.; Lepkowski, J. M.; Curtin, R.; McBee, R.
- Response Behavior in a Video-Web Survey: A Mode Comparison Study; 2016; Haan, M.; Ongena, Y. P.; Vannieuwenhuyze, J. T. A.; de Glopper, 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.
- Evaluation of mode equivalence of the MSKCC Bowel Function Instrument, LASA Quality of Life, and Subjective...; 2016; Bennett, A. V.; Keenoy, K.; Shouery, M.; Basch, E.; Temple, L. K.
- Making use of Internet interactivity to propose a dynamic presentation of web questionnaires; 2016; Revilla, M.; Ochoa, C.; Turbina, A.
- A streamlined approach to online linguistic surveys; 2016; Erlewine, M. Y.; Kotek, H.
- Du kommst hier nicht rein: Türsteherfragen identifizieren nachlässige Teilnehmer in Online-Umfragen; 2016; Merkle, B.; Kaczmirek, L.; Hellwig, O.
- Incorporating eye tracking into cognitive interviewing to pretest survey questions; 2016; Neuert, C.; Lenzner, T.
- Population Survey Features and Response Rates: A Randomized Experiment; 2016; Guo, Y.; Kopec, J.; Cibere, J.; Li, L. C.; Goldsmith, C. H.
- Mode Effect and Response Rate Issues in Mixed-Mode Survey Research: Implications for Recreational Fisheries...; 2016; Wallen, K. E.; Landon, A. C.; Kyle, G. T.; Schuett, M. A.; Leitz, J.; Kurzawski, K.
- A measure of survey mode differences; 2016; Homola, J.; Jackson, N. M.; Gill, Je.
- Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems ; 2016; Romano, M. F.; Sardella, M. V.; Alboni, F.
- Smartphones vs PCs: Does the Device Affect the Web Survey Experience and the Measurement Error for...; 2016; Toninelli, D.; Revilla, M.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.