Web Survey Bibliography
The visual capabilities offered by the internet provide a platform by which magazine readers may be queried about their viewing, noting and recognizing of ad copy appearing in specific magazine issues. However, it is well known that “samples” used in these studies may be subject to substantial bias arising from the non-probability nature of the sample selection process. Furthermore, when correctly computed, the response rates on many internet panels are quite low. In those situations when certain key variables are statistically linked (i.e. strongly correlated) with sample selection bias and key substantive outcomes, these variables may be used to adjust or calibrate these estimates. This is sometimes known as post stratification in traditional full-probability sampling and model-based estimation for model based (non-probability) sampling. In examining a large number of internet samples used to collect data on ad-noting and ad recognition it is has been found that these outcome measures are associated and correlated, to varying degrees, with gender, time spent reading, place of reading, percent of pages opened, and frequency of reading. Furthermore, we have found the distribution of these variables among internet respondents is substantially different from those in traditional full-probability surveys. We have developed a series of sample weighting procedures to remove a substantial amount of the “selection bias” linked to these reading qualities. This bias reduction step results in meaningful changes in readership ad-noting and ad identification. This paper will show, using actual data, how our approach to bias reduction weighting was developed, and how it impacts the outcomes of ad-noting and identification. In our decision to apply these weights we have adopted a standard minimization of mean squared error approach and perspective. That is, any weighting which increases variable random error must be offset with bias reduction. Bias reduction occurs when changes in the survey estimates are observed. Within a single magazine issue, the overall changes in ad noting scores are not typically large. However, there are ads in which noting scores do show substantial change. These changes are consistent with expectations linked to the adjustment measures. Furthermore, while an outside validation of the model based estimates has not been undertaken, our examination of overall impact across magazines is highly consistent with those expected on the basis of the variables involved. Thus, while we do not claim that our results are externally validated, we are comfortable in saying that the adjustments are in the expected direction and appear to make sense.
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Web survey bibliography (431)
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.
- Oversampling as a methodological strategy for the study of self-reported health among lesbian, gay and...; 2017; Anderssen, N.; Malterud, K.
- Analyzing Survey Characteristics, Participation, and Evaluation Across 186 Surveys in an Online Opt-...; 2017; Revilla, M.
- Comparison of response patterns in different survey designs: a longitudinal panel with mixed-mode and...; 2017; Ruebsamen, N.; Akmatov, M. K.; Castell, S.; Karch, A.; Mikolajczyk, R. T.
- Determinants of polling accuracy: the effect of opt-in Internet surveys; 2017; Sohlberg, J.; Gilljam, M.; Martinsson, J.
- Article Establishing an Open Probability-Based Mixed-Mode Panel of the General Population in Germany...; 2017; Bosnjak, M.; Dannwolf, T.; Enderle, T.; Schaurer, I.; Struminskaya, B.; Tanner, A.; Weyandt, K.
- Effects of Mobile versus PC Web on Survey Response Quality: a Crossover Experiment in a Probability...; 2017; Antoun, C.; Couper, M. P.; G. G.Conrad, F. G.
- 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.
- Targeted Appeals for Participation in Letters to Panel Survey Members; 2016; Lynn, P.
- Motivated Misreporting in Web Panels; 2016; Bach, R.; Eckman, S.
- Using official surveys to reduce bias of estimates from nonrandom samples collected by web surveys; 2016; Beresovsky, V.; Dorfman, A.; Rumcheva, P.
- A Feasibility Study of Recruiting and Maintaining a Web Panel of People with Disabilities; 2016; Chandler, J.
- 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.
- Integration of a phone-based household travel survey and a web-based student travel survey; 2016; Verreault, H.; Morency, C.
- Are Final Comments in Web Survey Panels Associated with Next-Wave Attrition?; 2016; McLauchlan, C.; Schonlau, M.
- Estimation and Adjustment of Self-Selection Bias in Volunteer Panel Web Surveys ; 2016; Niu, Ch.
- Participation in an Intensive Longitudinal Study with Weekly Web Surveys Over 2.5 Years; 2016; Barber, J. S.; Kusunoki, Y.; Gatny, H. H.; Schulz, P.
- The impact of survey duration on completion rates among Millennial respondents ; 2016; Coates, D.; Bliss, M.; Vivar, X.
- Cognitive Probing Methods in Usability Testing – Pros and Cons; 2016; Nichols, E. M.
- Assessing the Accuracy of 51 Nonprobability Online Panels and River Samples: A Study of the Advertising...; 2016; Yang,Y.;Callegaro,M.;Yang,Y.;Callegaro,M.;Chin,K.;Yang,Y.;Villar,A.;Callegaro, M.; Chin, K.; Krosnick...
- Calculating Standard Errors for Nonprobability Samples when Matching to Probability Samples ; 2016; Lee, Ad.; ZuWallack, R. S.
- User Experience and Eye-tracking: Results to Optimize Completion of a Web Survey and Website Design ; 2016; Walton, L.; Ricci, K.; Libman Barry, A.; Eiginger, C.; Christian, L. M.
- Using Web Panels to Quantify the Qualitative: The National Center for Health Statistics Research and...; 2016; Scanlon, P. J.
- Does Changing Monetary Incentive Schemes in Panel Studies Affect Cooperation? A Quasi-experiment on...; 2016; Schaurer, I.; Bosnjak, M.
- Web Probing for Question Evaluation: The Effects of Probe Placement ; 2016; Fowler, S.; Willis, G. B.; Moser, R. P.; Townsend, R. L. M.; Maitland, A.; Sun, H.; Berrigan, D.
- Using Cash Incentives to Help Recruitment in a Probability Based Web Panel: The Effects on Sign Up Rates...; 2016; Krieger, U.
- Making Connections on the Internet: Online Survey Panel Communications ; 2016; Libman Barry, A.; Eiginger, C.; Walton, L.; Ricci, K.
- Evaluating a Modular Design Approach to Collecting Survey Data Using Text Messages ; 2016; West, B. T.; Ghimire, D.; Axinn, W.
- Safety First: Ensuring the Anonymity and Privacy of Iranian Panellists’ While Creating Iran...; 2016; Farmanesh, A.; Mohseni, E.
- Tracking the Representativeness of an Online Panel Over Time ; 2016; Klausch, L. T.; Scherpenzeel, A.
- Non-Observation Bias in an Address-Register-Based CATI/CAPI Mixed Mode Survey; 2016; Lipps, O.
- 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.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Adaptive survey designs to minimize survey mode effects – a case study on the Dutch Labor Force...; 2016; Calinescu, M.; Schouten, B.
- 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.
- The Utility of an Online Convenience Panel for Reaching Rare and Dispersed Populations; 2016; Sell, R.; Goldberg, S.; Conron, K.
- 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.
- Reducing Underreports of Behaviors in Retrospective Surveys: The Effects of Three Different Strategies...; 2016; Lugtig, P. J.; Glasner, T.; Boeve, A.
- Dropouts in Longitudinal Surveys; 2016; Lugtig, P. J.; De Leeuw, E. D.