# Web Survey Bibliography

Collecting information from sampled units over the Internet or by mail is much more cost‒efficient than conducting interviews. These methods make self‒enumeration an attractive data‒collection method for surveys and censuses. Despite the benefits associated with self‒enumeration data collection—in particular Internet-based data collection—self‒enumerationcan produce low response rates compared to interviews. To increase response rates, non‒respondents are subject to a mixedmode of follow‒up treatments, which influence the resulting probability of response, to encourage them to participate. Because response occurrence is intrinsically conditional, we preliminary record response occurrence in discrete intervals, and we then characterize the probability of response by a discrete time hazard. This approach facilitates examining when a response is most likely to occur and how the probability of responding varies over both time and follow‒up treatments. Weuse regression analysis to investigate the effect of mixed‒mode on the response probability. Factors and interactions arecommonly treated in regression analyses, and have important implications for the interpretation of statistical models. The nonresponse bias can be avoided by multiplying the sampling weight of respondents by the inverse of an estimate of the response probability. Estimators and associated variance estimators of model parameters as well as of parameters of interest are studied. We take into account correlation over time for the same unit in variance estimation. The problem of optimal resources allocation within stages of the survey design is also investigated.Collecting information from sampled units over the Internet or by mail is much more cost‒efficient than conducting interviews. These methods make self‒enumeration an attractive data‒collection method for surveys and censuses. Despite the benefits associated with self‒enumeration data collection—in particular Internet-based data collection self‒enumerationcan produce low response rates compared to interviews. To increase response rates, non‒respondents are subject to a mixedmode of follow‒up treatments, which influence the resulting probability of response, to encourage them to participate. Because response occurrenceis intrinsically conditional, we preliminary record response occurrence in discrete intervals, and we then characterize the probability of response by a discrete time hazard. This approach facilitates examining when a response is most likely to occur and how the probability of responding varies over both time and follow‒up treatments. Weuse regression analysis to investigate the effect of mixed‒mode on the response probability. Factors and interactions are commonly treated in regression analyses, and have important implications for the interpretation of statistical models. The nonresponse bias can be avoided by multiplying the sampling weight of respondents by the inverse of an estimate of the response probability. Estimators and associated variance estimators of model parameters as well as of parameters of interest are studied. We take into account correlation over time for the same unit in variance estimation. The problem of optimal resources allocation within stages of the survey design is also investigated.

# Web survey bibliography (4086)

- Web Surveys Versus Other Survey Modes: An Updated Meta-analysis Comparing Response Rates ; 2016; Wengrzik, J.; Bosnjak, M.; Lozar Manfreda, K.
- The Effect of a Pre-due Date Reminder Letter on Non response in a Business Survey ; 2016; Hernandez, A. D.; Fan, C. C.; Tuttle, A.
- Adapting the Alternative Questionnaire Experiment for a Telephone Survey: Preparing for Changes to the...; 2016; Patten, E.; Brown, A.; Parker, K.
- Retrospective Measurement of Students’ Extracurricular Activities with a Self-administered Calendar...; 2016; Furthmueller, P.
- Privacy Concerns in Responses to Sensitive Questions. A Survey Experiment on the Influence of Numeric...; 2016; Bader, F., Bauer, J., Kroher, M., Riordan, P.
- Ballpoint Pens as Incentives with Mail Questionnaires – Results of a Survey Experiment; 2016; Heise, M.
- Non-Observation Bias in an Address-Register-Based CATI/CAPI Mixed Mode Survey; 2016; Lipps, O.
- Spatial Modeling through GIS to Reveal Error Potent ial in Survey Data: Where, What and How Much ; 2016; English, N.; Ventura, I.; Bilgen, I.; Stern, M. J.
- Bees to Honey or Flies to Manure? How the Usual Subject Recruitment Exacerbates the Shortcomings of...; 2016; Snell, S. A., Hillygus, D. S.
- Thinking Inside the Box Visual Design of the Response Box Affects Creative Divergent Thinking in an...; 2016; Mohr, A. H.; Sell, A.; Lindsay, T.
- Detecting Insufficient Effort Responding with an Infrequency Scale: Evaluating Validity and Participant...; 2016; Huang, J. L.; Bowling, N. A.; Liu, Me.; Li, Yu.
- Detecting careless respondents in web-based questionnaires: Which method to use?; 2016; Niesen, A. S. M.; Meijer, R. R.; Tendeiro, J. N.
- Web surveys for offline rural communities ; 2016; Gichohi, B. W.
- On-line life history calendar and sensitive topics: A pilot study; 2016; Morselli, D.; Berchtold, A.; Granell, J.-C. S.; Berchtold, And.
- Does survey mode matter for studying electoral behaviour? Evidence from the 2009 German Longitudinal...; 2016; Bytzek, E.; Bieber, I. E.
- The impact of visual design and response formats on data quality in a web survey of MOOC students; 2016; Maloshonok, N.; Terentev, E.
- An experiment comparing grids and item-by-item formats in web surveys completed through PCs and smartphones...; 2016; Revilla, M.; Toninelli, D.; Ochoa, C.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Gamifying Questions Using Text Alone; 2016; Cape, P. J.
- Assessing the Effects of Participant Preference and Demographics in the Usage of Web-based Survey Questionnaires...; 2016; Mlikotic, R.; Parker, B.; Rajapakshe, R.
- Improving Inpatient Surveys: Web-Based Computer Adaptive Testing Accessed via Mobile Phone QR Codes; 2016; Chien, T. S.; Lin, W.S.
- Surveying End-of-Life Medical Decisions in France: Evaluation of an Innovative Mixed-Mode Data Collection...; 2016; Legleye, S; Pennec, S.; Monnier, A.; Stephan, A.; Brouard, N.; Bilsen, J.; Cohen, J.
- Problems and Prospects in Survey Research; 2016; Moy, P.; Murphy, J.
- When will Nonprobability Surveys Mirror Probability Surveys? Considering Types of Inference and Weighting...; 2016; Pasek, J.
- Eye-tracking Social Desirability Bias; 2016; Kaminska, O.; Foulsham, T.
- Evaluating Three Approaches to Statistically Adjust for Mode Effects; 2016; Kolenikov, S.; Kennedy, C.
- Distractions: The Incidence and Consequences of Interruptions for Survey Respondents ; 2016; Ansolabehere, S.; Schaffner, B. F.
- The Effect of CATI Questions, Respondents, and Interviewers on Response Time; 2016; Olson, K.; Smyth, J. D.
- Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey:...; 2016; Dal Grande, E.; Chittleborough, C. R.; Campostrini, S.; Dollard, M.; Taylor, A. W.
- Pitfalls, Potentials, and Ethics of Online Survey Research: LGBTQ and Other Marginalized and Hard-to...; 2016; McInroy, L. B.
- Effects of Personalization and Invitation Email Length on Web-Based Survey Response Rates; 2016; Trespalacios, J. H.; Perkins, R. A.
- Linearization Variance Estimators for Mixed ‒ mode Survey Data when Response Indicators are Modeled...; 2016; Demnati, A.
- Forecasting proportional representation elections from non-representative expectation surveys; 2016; Graefe, A.
- Short and Sweet? Length and Informative Content of Open-Ended Responses Using SMS as a Research Mode; 2016; Walsh, E.; Brinker, J. K.
- Adaptive survey designs to minimize survey mode effects – a case study on the Dutch Labor Force...; 2016; Calinescu, M.; Schouten, B.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- What is the gain in a probability-based online panel to provide Internet access to sampling units that...; 2016; Revilla, M.; Cornilleau, A.; Cousteaux, A-S.; Legleye, S; de Pedraza, P.
- Representative web-survey!; 2016; Linde, P.
- Assessing targeted approach letters: effects in different modes on response rates, response speed and...; 2016; Lynn, P.
- New Generation of Online Questionnaires?; 2016; Revilla, M.; Ochoa, C.; Turbina, A.
- The Analysis of Respondent’s Behavior toward Edit Messages in a Web Survey; 2016; Park, Y.
- Refining the Web Response Option in the Multiple Mode Collection of the American Community Survey; 2016; Hughes, T.; Tancreto, J.
- The Utility of an Online Convenience Panel for Reaching Rare and Dispersed Populations; 2016; Sell, R.; Goldberg, S.; Conron, K.
- Assessment of Innovations in Data Collection Technology for Understanding Society; 2016; Couper, M. P.
- Comparing online and telephone survey results in the context of a skin cancer prevention campaign evaluation...; 2016; Hollier, L.P.; Pettigrew, S.; Slevin, T.; Strickland, M.; Minto, C.
- Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk; 2016; Berinsky, A.; Huber, G. A.; Lenz, G. S.
- Setting Up an Online Panel Representative of the General Population The German Internet Panel; 2016; Blom, A. G.; Gathmann, C.; Krieger, U.
- Implementation of Web-Based Respondent Driven Sampling among Men Who Have Sex with Men in Sweden; 2016; Stroemdahl, S.; Lu, X.; Bengtsson, L.; Liljeros, F.; Thorson, A.
- Options for Fielding and Analyzing Web Surveys; 2016; Schonlau, M.; 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...