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 (366)
- Grundzüge des Datenschutzrechts und aktuelle Datenschutzprobleme in der Markt- und Sozialforschung; 2017; Schweizer, A.
- Web- and Phone-based Data Collection using Planned Missing Designs; 2017; Revelle, W.; Condon, M. D.; Wilt, J.; French, A. J.; Brown, A.; Elleman, G. L.
- Finding and Investigating Geographical Data Online; 2017; Martin, D.; Cockings, S.; Leung, S.
- CAQDAS at a Crossroads: Affordances of Technology in an Online Environment; 2017; Silver, C.; Bulloch, L. S.
- Artificial Intelligence/Expert Systems and Online Research; 2017; Brent, E.
- Improving the Effectiveness of Online Data Collection by Mixing Survey Modes; 2017; Dillman, D. A.; Hao, F.; Millar, M. M.
- Online Survey Software; 2017; Kaczmirek, L.
- Online Survey Design; 2017; To, N.
- Sampling Methods for Online Surveys; 2017; Fricker, R. D.
- Research Design and Tools for Online Research; 2017; Hewson, C. M.
- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
- Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention; 2016; Kuhlmann, T.; Reips, U.-D.; Wienert, J.; Lippke, S.
- 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.
- Facebook, Twitter, & Qr codes: An exploratory trial examining the feasibility of social media mechanisms...; 2016; Gu, L. L.; Skierkowski, D.; Florin, P.; Friend, K.; Ye, Y.
- Distractions: The Incidence and Consequences of Interruptions for Survey Respondents ; 2016; Ansolabehere, S.; Schaffner, B. F.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- Representative web-survey!; 2016; Linde, P.
- 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.
- 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.
- Sample Representation and Substantive Outcomes Using Web With and Without Incentives Compared to Telephone...; 2016; Lipps, O.; Pekari, N.
- Effects of Data Collection Mode and Response Entry Device on Survey Response Quality; 2016; Ha, L.; Zhang, Che.; Jiang, W.
- Navigation Buttons in Web-Based Surveys: Respondents’ Preferences Revisited in the Laboratory; 2016; Romano Bergstrom, J. C.; Erdman, C.; Lakhe, S.
- Web-based versus Paper-based Survey Data: An Estimation of Road Users’ Value of Travel Time Savings...; 2016; Kato, H.; Sakashita, A.; Tsuchiya, Tak.
- Reminder Effect and Data Usability on Web Questionnaire Survey for University Students; 2016; Oishi, T.; Mori, M.; Takata, E.
- 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.
- Participant recruitment and data collection through Facebook: the role of personality factors; 2016; Rife, S. C.; Cate, K. L.; Kosinski, M.; Stillwell, D.
- What drives the participation in a monthly research web panel? The experience of ELIPSS, a French random...; 2016; Legleye, S; Cornilleau, A.; Razakamanana, N.
- Quantifying Under- and Overreporting in Surveys Through a Dual-Questioning-Technique Design. ; 2016; de Jong , M.; Fox, J.-P.; Steenkamp, J. - B. E. M.
- Take the money and run? Redemption of a gift card incentive in a clinician survey. ; 2016; Chen, J. S.; Sprague, B. L.; Klabunde, C. N.; Tosteson, A. N. A.; Bitton, A.; Onega, T.; MacLean, C....
- Electronic and paper based data collection methods in library and information science research: A comparative...; 2016; Tella, A.
- A Technical Guide to Effective and Accessible web Surveys; 2016; Baatard, G.
- The Validity of Surveys: Online and Offline; 2016; Wiersma, W.
- Methods can matter: Where Web surveys produce different results than phone interviews; 2016; Keeter, S.
- Computer-assisted and online data collection in general population surveys; 2016; Skarupova, K.
- Will They Stay or Will They Go? Personality Predictors of Dropout in Online Study; 2016; Nestler, S.; Thielsch, M.; Vasilev, E.; Back, M.
- A Framework of Incorporating Thai Social Networking Data in Online Marketing Survey; 2016; Jiamthapthaksin, R.; Aung, T. H.; Ratanasawadwat, N.
- Development of a scale to measure skepticism toward electronic word-of-mouth; 2016; Zhang, Xia.; Ko, M.; Carpenter, D.
- Improving social media measurement in surveys: Avoiding acquiescence bias in Facebook research; 2016; Kuru, O.; Pasek, J.
- Psychological research in the internet age: The quality of web-based data; 2016; Ramsey, S. R.; Thompson, K. L.; McKenzie, M.; Rosenbaum, A.
- Internet Abusive Use Questionnaire: Psychometric properties; 2016; Calvo-Frances, F.
- Revisiting “yes/no” versus “check all that apply”: Results from a mixed modes...; 2016; Nicolaas, G.; Campanelli, P.; Hope, S.; Jaeckle, A.; Lynn, P.
- A Statistical Approach to Provide Individualized Privacy for Surveys; 2016; Esponda, F.; Huerta, K.; Guerrero, V. M.
- Online and Social Media Data As an Imperfect Continuous Panel Survey; 2016; Diaz, F.; Garmon, F.; Hofman, J. K.; Kiciman, E.; Rothschild, D.