Web Survey Bibliography
Title Assessing Potential Bias in Respondent-driven Incident Based Data from a Web Survey of College Students
Year 2016
Access date 06.06.2016
Abstract
Incident-level data collection is a useful approach when measuring events that can occur multiple times within a survey’s reference period. Incident-based data allow survey researchers to analyze not just characteristics of persons but also characteristics of incidents (e.g. to assess the proportion of victimizations reported to authorities). However, asking respondents to complete detailed incident reports for all incidents experienced within the reference period may be too burdensome for persons who experienced several incidents. Therefore, survey practitioners often cap the number of
incident reports required for each respondent. If reported incidents differ from those not covered in the survey instrument, then bias potentially exists because limiting the number of incidents seemingly excluded incidents with certain characteristics. The Campus Climate Survey Validation Study (CCSVS), sponsored by the Bureau of Justice Statistics and the Office of Violence Against Women, was a web-based survey administered at nine colleges that collected prevalence and incident-based information onunwanted sexual contact. The CCSVS capped the number of incident reports at three and allowed respondents to determine the order in which incident reports were completed. To assess the potential for bias, we determine whether respondents systematically ordered the reported incidents. Bias could be introduced if the incidents that do not have a completed incident report are fundamentally different (e.g., occur later in the year or are less severe) than those that were reported. We consider incident ordering based on the chronological order and severity of incidents. In addition, we assess whether respondents who were unsure of the month in which one of their incidents occurred reported those incidents in a systematic way. Our analysis found that respondents do appear to systematically order their incidents both in terms of chronological order and severity. We quantify the potential impact of this biased ordering on key victimization estimates.
incident reports required for each respondent. If reported incidents differ from those not covered in the survey instrument, then bias potentially exists because limiting the number of incidents seemingly excluded incidents with certain characteristics. The Campus Climate Survey Validation Study (CCSVS), sponsored by the Bureau of Justice Statistics and the Office of Violence Against Women, was a web-based survey administered at nine colleges that collected prevalence and incident-based information onunwanted sexual contact. The CCSVS capped the number of incident reports at three and allowed respondents to determine the order in which incident reports were completed. To assess the potential for bias, we determine whether respondents systematically ordered the reported incidents. Bias could be introduced if the incidents that do not have a completed incident report are fundamentally different (e.g., occur later in the year or are less severe) than those that were reported. We consider incident ordering based on the chronological order and severity of incidents. In addition, we assess whether respondents who were unsure of the month in which one of their incidents occurred reported those incidents in a systematic way. Our analysis found that respondents do appear to systematically order their incidents both in terms of chronological order and severity. We quantify the potential impact of this biased ordering on key victimization estimates.
Access/Direct link Conference Homepage (abstract)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - 2016 (264)
- Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems; 2017; Romano, M. F.; Sardella, M. V.; Alboni, F.
- 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.