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 - Survey Practice (65)
- Device and Internet Use among Spanish-dominant Hispanics: Implications for Web Survey Design and Testing...; 2017; Trejo, Y. A. G.; Schoua-Glusberg, A.
- Improving survey response rates: The effect of embedded questions in web survey email Invitations; 2017; Liu, M.; Inchausti, N.
- Mobile-only web survey respondents; 2016; Lugtig, P. J.; Toepoel, V.; Amin, A.
- Effect of a Pre-Paid Incentive on Response Rates to an Address-Based Sampling (ABS) Web-Mail Survey; 2016; Suzer-Gurtekin, Z.; Elkasabi, M.; Liu, Me.; Lepkowski, J. M.; Curtin, R.; McBee, R.
- Adaptive survey designs to minimize survey mode effects – a case study on the Dutch Labor Force...; 2016; Calinescu, M.; Schouten, B.
- Navigation Buttons in Web-Based Surveys: Respondents’ Preferences Revisited in the Laboratory; 2016; Romano Bergstrom, J. C.; Erdman, C.; Lakhe, S.
- Collecting Data from mHealth Users via SMS Surveys: A Case Study in Kenya; 2016; Johnson, D.
- “Money Will Solve the Problem”: Testing the Effectiveness of Conditional Incentives for...; 2016; DeCamp, W.; Manierre, M. J.
- Effects of Incentive Amount and Type of Web Survey Response Rates; 2016; Coopersmith, J.; Vogel, L. K.; Bruursema, T.; Feeney, K.
- Effect of a Post-paid Incentive on Response to a Web-based Survey; 2016; Brown, J. A.; Serrato, C. A.; Hugh, M.; Kanter, M. H.; A.; Spritzer, K. L.; Hays, R. D.
- The 2013 Census Test: Piloting Methods to Reduce 2020 Census Costs; 2016; Walejko, G. K.; Miller, P. V.
- Computers, Tablets, and Smart Phones: The Truth About Web-based Surveys; 2016; Merle, P.; Gearhart, S.; Craig, C.; Vandyke, M.; Brooks, M. E.; Rahimi, M.
- Scientific Surveys Based on Incomplete Sampling Frames and High Rates of Nonresponse; 2016; Fahimi, M.; Barlas, F. M.; Thomas, R. K.; Buttermore, N. R.
- Probabilistic Web Survey Methodology in Education Centers: An Example in Spanish Schools; 2015; Tapia, J. A., Menendez, J. A.
- Understanding Participation in a Web-Based Measurement Burst Design: Response Metrics and Predictors...; 2015; Griffin, J., Patrick, M. E.
- Rating Scales in Survey Research: Using the Rasch model to illustrate the middle category measurement...; 2015; Bradley, K. D., Peabody, M. R., Akers, K. S., Knutson, N. M.
- Facebook as a Tool for Respondent Tracing; 2015; Schneider, S. J., Burke-Garcia, A., Thomas, G.
- Social Science Survey Methodology Training: Understanding the Past and Assessing the Present to Shape...; 2015; Jans, M., Meyers, M., Fricker, S.
- Future Training of Survey Methodologists; 2015; Kolenikov, S., Jans, M., O'Hare, B. C., Fricker, S.
- Survey participation via mobile devices in a probability-based online-panel: Prevalence, determinants...; 2014; Poggio, T., Bosnjak, M., Weyandt, K.
- Examining the Effect of Prenotification Postcards on Online Survey Response Rate in a University Graduate...; 2014; Lalasz, C. B., Doane, M. J., Springer, V. A., Dahir, V. B.
- The Effectiveness of Mailed Invitations for Web Surveys and the Representativeness of Mixed-Mode versus...; 2014; Bandilla, W., Couper, M. P., Kaczmirek, L.
- Respondent-Driven Sampling of Heterosexuals at Increased Risk of HIV Infection; 2014; Batra, P., Gray, S. C., Krishna, N., Prachand, N., Robinson, W. T., Wejnert, C.
- Two Are Better Than One: The Use of a Mixed-Mode Data Collection to Improve the Electoral Forecast; 2014; de Rada, V. D., Pasadas del Amo, S.
- Recent Books and Journals in Public Opinion, Survey Methods, and Survey Statistics; 2014; Callegaro, M.
- Undisclosed Privacy: The Effect of Privacy Rights Design on Response Rates; 2014; Haer, R., Meidert, N.
- The Smartphone Way to Collect Survey Data; 2013; Stapleton, C.
- Measuring Compliance in Mobile Longitudinal Repeated-Measures Design Study; 2013; Link, M. W.
- Is everyone able to use a smartphone in survey research?; 2013; Fernee, H., Sonck, N.
- A Comparison of Data Quality Across Modes in a Mixed-Mode Collection of Administrative Records; 2013; Worthy, M., Mayclin, D.
- Reconceptualizing Survey Representativeness for Evaluating and Using Nonprobability Samples; 2013; Fan, D. P.
- To Click, Type, or Drag? Evaluating Speed of Survey Data Input Methods; 2013; Husser, J. A., Husser, J. A.
- The smart(phone) way to collect survey data; 2013; Stapleton, C.
- Do Mail and Internet Surveys Produce Different Item Nonresponse Rates? An Experiment Using Random Mode...; 2012; Millar, M. M., Dillman, D. A.
- Item Nonresponse in a Client Survey of the General Public; 2012; Israel, G. D., Lamm, A. J.
- Comparing Item Nonresponse across Different Delivery Modes in General Population Surveys; 2012; Lesser, V. M., Newton, L., Yang, D.
- Determinants of Item Nonresponse to Web and Mail Respondents in Three Address-Based Mixed-Mode Surveys...; 2012; Messer, B. L., Edwards, M. L., Dillman, D. A.
- Exploring Animated Faces Scales in Web Surveys: Drawbacks and Prospects; 2012; Emde, M., Fuchs, M.
- Smart Surveys for Smart Phones: Exploring Various Approaches for Conducting Online Mobile Surveys via...; 2012; Buskirk, T. D., Andrus, C.
- Using Facebook to Locate Sample Members; 2011; Rhodes, B. B., Marks, E. L.
- Paradata in Survey Research; 2011; West, B. T.
- Towards Usage of Avatar Interviewers in Web Surveys; 2011; Jans, M., Malakhoff, L.
- Making Good Use of Survey Paradata; 2010; Lynn, P., Nicolaas, G.
- Multi-Mode and Method Experiment in a Study of Nurses; 2010; Friese, C. R., Lee, C. S., O'Brien, S., Crawford, S. D.
- An Experiment With an Employment Sector Question; 2010; Finno, A. A., Kohout, J.
- Lottery Incentives and Online Survey Response Rates; 2010; Preece, M. J., Johanson, G., Hitchcock, J.
- Practical Considerations in Raking Survey Data; 2009; Battaglia, M. P., Hoaglin, D.C, Franklin, P. D.
- Cell Phone Mainly Households: Coverage and Reach for Telephone Surveys Using RDD Landline Samples; 2009; Boyle, J., Lewis, F., Tefft, B.
- Declining Working Phone Rates Impact Sample Efficiency; 2009; Piekarski, L.
- Using Non-Probability Samples for Confusion Surveys - Mall Intercepts and the Internet; 2009; Ericksen, E. P.