Web Survey Bibliography
Title Dynamic Instructions in Check-All-That-Apply Questions
Year 2016
Access date 08.06.2016
Abstract
In check-all-that-apply questions, respondents are required to select all applicable responses. Although check-all-that-apply questions are one of
the most commonly used question formats in (Web) surveys, respondents often do not spend sufficient effort to thoroughly process each of the response alternatives. Instead, respondents tend to select one of the first alternatives without sufficiently considering the remaining ones, resulting in primacy effects and an overall lower number of responses then actually apply to them. On the contrary, respondents may select response alternatives that apply to them only vaguely, resulting in a considerably higher number of responses than desired. In order toensure comparability researchers often use instructions with check-all-that-apply questions that specify the number of responses desired (e.g., "Please select the three most important aspects."). However, such instructions are often overlooked or ignored by respondents. Web surveys offer the opportunity toimplement dynamic design features that possibly increase the respondents’ attention to such instructions. In this paper, we assess the effectiveness of instant feedback messages appearing once respondents start answering a question. Using a between-subjects design, the effectiveness of providing instructions either in the form of static instructions that are always visible together with the question stem (EG1), dynamic instructions that instantly appear once respondents start answering the question (EG2), or a combination of both (EG3) was assessed. Experimental conditions were evaluated compared to a control group where no instruction was provided (CG). Initial findings concerning the effectiveness of different instruction types showed that a combination of both static and dynamic instructions is most effective in obtaining the desired number of responses. Comparisons with importance ratings shed lighton the question whether respondents actually select the most important responses. In addition, response order effects were assessed in order to determine the extent of satisficing behavior in each experimental condition.
the most commonly used question formats in (Web) surveys, respondents often do not spend sufficient effort to thoroughly process each of the response alternatives. Instead, respondents tend to select one of the first alternatives without sufficiently considering the remaining ones, resulting in primacy effects and an overall lower number of responses then actually apply to them. On the contrary, respondents may select response alternatives that apply to them only vaguely, resulting in a considerably higher number of responses than desired. In order toensure comparability researchers often use instructions with check-all-that-apply questions that specify the number of responses desired (e.g., "Please select the three most important aspects."). However, such instructions are often overlooked or ignored by respondents. Web surveys offer the opportunity toimplement dynamic design features that possibly increase the respondents’ attention to such instructions. In this paper, we assess the effectiveness of instant feedback messages appearing once respondents start answering a question. Using a between-subjects design, the effectiveness of providing instructions either in the form of static instructions that are always visible together with the question stem (EG1), dynamic instructions that instantly appear once respondents start answering the question (EG2), or a combination of both (EG3) was assessed. Experimental conditions were evaluated compared to a control group where no instruction was provided (CG). Initial findings concerning the effectiveness of different instruction types showed that a combination of both static and dynamic instructions is most effective in obtaining the desired number of responses. Comparisons with importance ratings shed lighton the question whether respondents actually select the most important responses. In addition, response order effects were assessed in order to determine the extent of satisficing behavior in each experimental condition.
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.