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 (4086)
- ESS Handbook for Quality Reports; 2009
- ESS Standard for Quality Reports; 2009
- Guest Blog: More on the Problems with Opt-in Internet Surveys; 2009; Langer, G.
- Psychological Factors Affecting Perceptions of Unsolicited Commercial E-mail; 2009; Morimoto, M., Chang, S.
- Innovations in Social Science Research Methods; 2009; Xenitidou, M., Gilbert, N.
- Where Is the unproctored Internet testing train headed now?; 2009; Tippins, N. T.
- Statistical disclosure control for survey data; 2009; Skinner, C.
- Response format effects on measurement of employment; 2009; Thomas, R. K., Dillman, D. A., Smyth, J. D.
- Preserving the integrity of online testing; 2009; Burke, E.
- Mobile surveys from a technological perspective; 2009; Pferdekämper, T., Batanic, B.
- MarketTools TrueSample; 2009
- ISO 26362 Access panels in market, opinion, and social research-Vocabulary and service requirements; 2009
- Internet alternatives to traditional proctored testing: Where are we now?; 2009; Tippins, N. T.
- From the Editor; 2009; Sackett, P. R.
- Exploring mode effects in a panel survey of new businesses; 2009; Santos, B., DesRoches, D.
- Dirty little secrets of online panels. And how the one you select can make or break your study; 2009
- comScore Media Metrix U.S. Methodlogy. An ARF research review; 2009; Cook, W. A., Pettit, R.
- Can we make official statistics with self-selection web surveys?; 2009; Bethlehem, J.
- Attitudes over time: The psychology of panel conditioning; 2009; Sturgis, P., Allum, N., Brunton-Smith, I.
- Association collaborative effort releases online research definitions, expands membership; 2009
- The Effect of Phrasing Scale Items in Low-Brow or High-Brow Language on Responses; 2009; Blasius, J., Friedrichs, J.
- Question and Questionnaire Design; 2009; Krosnick, J. A., Presser, S.
- Attrition in Consumer Panels; 2009; Tortora, R. D.
- Sample Design for Understanding Society ; 2009; Lynn, P.
- The 2008 Confirmit Annual Market Research Software Survey; 2009; Macer, T., Wilson, S.
- Predicting Tie Strength With Social Media; 2009; Karahalios, K., Gilbert, Er.
- A Special Report from the Advertising Research Foundation - The Foundations of Quality Initiative: A...; 2009; Walker, R., Pettit, R., Rubinson, J.
- A Web-Based Tool for Assessing and Improving the Usefulness of Community Health Assessments; 2009; Stoto, M. A., Straus, S. G., Bohn, C., Irani, P.
- The rise of survey sampling; 2009; Bethlehem, J.
- Using an ABS frame to recruit a probability-based online panel; 2009; DiSogra, C.
- Address Based Sampling: How to Do It, Practical Tips; 2009; Dutwin, D.
- Use of Incentives in Survey Research; 2009; Lavrakas, P. J.
- Stochastic properties of the Internet sample; 2009; Getka-Wilczynska, E.
- Continuous Measurement of Musically-Induced Emotion: A Web Experiment ; 2009; Egermann, H., Nagel, F., Altenmueller, E., Kopiez, R.
- E-epidemiology : Adapting epidemiological methods for the 21st century; 2009; Bexelius, C.
- Web based survey: an emerging tool; 2009; Srivenkataramana, T., Saisree, M.
- The Use of Online Methodologies in Data Collection for Gambling and Gaming Addictions; 2009; Griffiths, M. D.
- Questasy: Online Survey Data Dissemination Using DDI 3; 2009; de Bruijne, M., Amin, A.
- Methodeneffekte von Web-Befragungen: Soziale Erwünschtheit vs. Soziale Entkontextualisierung; 2009; Taddicken, M.
- Response Mode and Bias Analysis in the IRS’ Individual Taxpayer Burden Survey; 2009; Brick, J. M., Contos, G., Masken, K., Nord, R.
- Survey Mode Effects in Two Military Surveys; 2009; Yang, M., Falcone, A. E., Milan, L. M.
- Measuring Internet And Press Audience In The Media Convergence Era. In Search Of A New Paradigm In Researching...; 2009; Pawlak, J., Póltorak, M.
- The Decline And Fall Of The Response Rate: The Fightback ; 2009; Green, A., Staplehurst, S., Windle, R.
- Internet Measurement of Ad-noting: Sampling and Statistical Issues; 2009; Frankel, M. R., Baim, J., Galin, M., Agresti, J., Augemberg, K.
- Audipresse Premium: Using The Internet To Help Measure Press Readership ; 2009; Saint-Joanis, G., Néraudau, J.
- Questionnaire Intelligence: New Rules Of Engagement For Online Survey Design ; 2009; McMahon, L., Stamp, R.
- Presenting Publications in Online Print Audience Questionnaires; 2009; Carroll, J., Collins, S., Farrer, N.
- Methodological Tests On Online Research: Incidence Of Formal Aspects/Questionnaire Layout On The Results...; 2009; Schmutz, B., Lê Van Truoc, O.
- Online Interviewing through Access Panel: Quantity and Quality Assurance; 2009; Petric, I., Appel, M., de Leeuw, E. D.
- The impact of gender in e-mailed survey invitations; 2009; Derham, P.