Web Survey Bibliography
Relevance & Research question: Smartphones are becoming increasingly important and widely-used for survey completion. Smartphones offer many new possibilities for survey research: We can, for example, send pop-up questions in real-time, for instance to measure participants' feelings, and record sensor data. However, as nice as these new opportunities are, the questions we ask can get increasingly intrusive, and we risk over-asking participants, who may choose to drop-out in response. Nonresponse and nonresponse bias may be different in different phases (f.e. survey, pop-up questions, consent to record sensor data) of the research, because of the different intrusiveness of every phase. Fundamental, methodological knowledge about nonresponse in smartphone-only studies is lacking, but very important to understand selection bias. Therefore, our main research question is: How can we predict nonresponse at different phases in a smartphone-only survey?
Methods & data: We studied an innovative smartphone-only Time Use Survey. The Dutch Institute for Social Research conducted their Time Use Survey in 2013 through an app on a smartphone at two randomly chosen days of the week.
The study consisted of four phases, specifically:
1. Invitation to participate in the study (n = 2154)
2. Participation in the Time Use Survey (n = 1610).
3. Answer pop-up questions (n = 1407).
4. Give permission to record sensor data (f.e. GPS locations and call data) (n = 1004).
Results: We documented the nonresponse and estimated nonresponse bias for each of the four phases. Because the data were collected in a panel, we can use predictors from earlier waves. Therefore, we do not only have the typical (age, sex, income) information, but also more interesting variables (personality, participation history, smartphone usage, survey attitude), which we can use as covariates and predictors of nonresponse.
Added value: This study provides us with knowledge about bias in smartphone-only studies, a field which remains relatively unexplored. We used participants from the LISS-panel, which aims to be representative of the Dutch population. Therefore, this knowledge about who does and who doesn’t participate, and how smartphone studies may be biased, can be very valuable for all online researchers who consider such a study.
Web survey bibliography (4086)
- Displaying Videos in Web Surveys: Implications for Complete Viewing and Survey Responses; 2017; Mendelson, J.; Lee Gibson, J.; Romano Bergstrom, J. C.
- Using experts’ consensus (the Delphi method) to evaluate weighting techniques in web surveys not...; 2017; Toepoel, V.; Emerson, H.
- Mind the Mode: Differences in Paper vs. Web-Based Survey Modes Among Women With Cancer; 2017; Hagan, T. L.; Belcher, S. M.; Donovan, H. S.
- Answering Without Reading: IMCs and Strong Satisficing in Online Surveys; 2017; Anduiza, E.; Galais, C.
- Ideal and maximum length for a web survey; 2017; Revilla, M.; Ochoa, C.
- Social desirability bias in self-reported well-being measures: evidence from an online survey; 2017; Caputo, A.
- Web-Based Survey Methodology; 2017; Wright, K. B.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
- Lessons from recruitment to an internet based survey for Degenerative Cervical Myelopathy: merits of...; 2017; Davies, B.; Kotter, M. R.
- Web Survey Gamification - Increasing Data Quality in Web Surveys by Using Game Design Elements; 2017; Schacht, S.; Keusch, F.; Bergmann, N.; Morana, S.
- Effects of sampling procedure on data quality in a web survey; 2017; Rimac, I.; Ogresta, J.
- Comparability of web and telephone surveys for the measurement of subjective well-being; 2017; Sarracino, F.; Riillo, C. F. A.; Mikucka, M.
- Achieving Strong Privacy in Online Survey; 2017; Zhou, Yo.; Zhou, Yi.; Chen, S.; Wu, S. S.
- A Meta-Analysis of the Effects of Incentives on Response Rate in Online Survey Studies; 2017; Mohammad Asire, A.
- Telephone versus Online Survey Modes for Election Studies: Comparing Canadian Public Opinion and Vote...; 2017; Breton, C.; Cutler, F.; Lachance, S.; Mierke-Zatwarnicki, A.
- Examining Factors Impacting Online Survey Response Ratesin Educational Research: Perceptions of Graduate...; 2017; Saleh, A.; Bista, K.
- Usability Testing for Survey Research; 2017; Geisen, E.; Romano Bergstrom, J. C.
- Paradata as an aide to questionnaire design: Improving quality and reducing burden; 2017; Timm, E.; Stewart, J.; Sidney, I.
- Fieldwork monitoring and managing with time-related paradata; 2017; Vandenplas, C.
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Interviewer Gender and Survey Responses: The Effects of Humanizing Cues Variations; 2017; Jablonski, W.; Krzewinska, A.; Grzeszkiewicz-Radulska, K.
- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
- Where, When, How and with What Do Panel Interviews Take Place and Is the Quality of Answers Affected...; 2017; Niebruegge, S.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance...; 2017; Wetzlehuetter, D.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- The Effect of Respondent Commitment on Response Quality in Two Online Surveys; 2017; Cibelli Hibben, K.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- Social Desirability and Undesirability Effects on Survey Response latencies; 2017; Andersen, H.; Mayerl, J.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.