Web Survey Bibliography
A survey sample may cover segments of the target population in proportions that do not match the proportions of those segments in the population itself. The differences may arise, for example, from sampling fluctuations, from nonresponse, or because the sample design was not able to cover the entire target population. In such situations one can often improve the relation between the sample and the population by adjusting the sampling weights of the cases in the sample so that the marginal totals of the adjusted weights on specified characteristics, referred to as control variables, agree with the corresponding totals for the population. This operation is known as raking ratio estimation (Deming 1943; Kalton 1983), raking, or sample-balancing, and the population totals are usually referred to as control totals. Raking is most often used to reduce biases from nonresponse and noncoverage in sample surveys.
Raking usually proceeds one variable at a time, applying a proportional adjustment to the weights of the cases that belong to the same category of the control variable. The initial design weights in the raking process are often equal to the inverse of the selection probabilities and may have undergone some adjustments for unit nonresponse and noncoverage. The weights from the raking process are used in estimation and analysis.
The adjustment to control totals is sometimes achieved by creating a cross-classification of the categorical control variables (e.g., age categories×gender×race×household-income categories) and then matching the total of the weights in each cell to the control total. This approach, however, can spread the sample thinly over a large number of adjustment cells. It also requires control totals for all cells of the cross-classification. Often this is not feasible (e.g., control totals may be available for age×gender×race but not when those cells are subdivided by household income).
The use of marginal control totals for single variables (i.e., each margin involves only one control variable) often avoids many of these difficulties. In return, of course, the two-variable (and higher-order) weighted distributions of the sample are not required to mimic those of the population.
The next two sections discuss the raking algorithm and its convergence. Subsequent sections discuss control totals and several issues that arise in practical applications: two-variable margins, raking at the state level in national surveys, maintaining adjustments for nonresponse and noncoverage, surveys that involve screening, and weight trimming.
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Web survey bibliography - 2009 (509)
- Creation and Usability Testing of a Web-Based Pre-Scanning Radiology Patient Safety and History Questionnaire...; 2016; Robinson, T. J.; DuVall, S.; Wiggins III, R
- Mixed Research as a Tool for Developing Quantitative Instruments; 2009; Onwuegbuzie, A. J.; Bustamante, R. M.; A. A.Nelson, J. A.
- Slider Scales in Online Surveys; 2009; Cape, P. J.
- User’s Guide to the Advance Release of the 2008-2009 ANES Panel Study ; 2009; DeBell, M.; Krosnick, J. A.; Lupia, A.; Roberts, C.
- The denominator problem: Estimating MSM-specific incidence of sexually transmitted infections and prevalence...; 2009; Marcus, U., Schmidt, A. J., Kollan, C., Hamouda, O.
- Survey Research in the United States: Roots and Emergence 1890-1960 ; 2009; Converse, P. D.
- Practical Considerations in Raking Survey Data; 2009; Battaglia, M. P., Hoaglin, D.C, Franklin, P. D.
- Methods for oversampling rare subpopulations in social surveys; 2009; Kalton, G.
- Start of the LISS panel: Sample and recruitment of a probability-based Internet panel ; 2009; Scherpenzeel, A.
- Comparing response rates in e-mail and paper surveys: A meta-analysis; 2009; Shih, T.-H., Fan, X.
- Recycling and waste minimisation behaviours of the transient student population in Oxford: results of...; 2009; Robertson, S., Walkington, H.
- 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.