# Web Survey Bibliography

In many sample surveys there are items requesting binary response (e.g., obese, not obese) from a number of small areas. Inference is required about the probability for a positive response (e.g., obese) in each area, the probability being the same for all individuals in each area and different across areas. Because of the sparseness of the data within areas, direct estimators are not reliable, and there is a need to use data from other areas to improve inference for a specific area. Essentially, a priori the areas are assumed to be similar, and a hierarchical Bayesian model, the standard beta-binomial model, is a natural choice. The innovation is that a practitioner may have much-needed additional prior information about a linear combination of the probabilities. For example, a weighted average of the probabilities is a parameter, and information can be elicited about this parameter, thereby making the Bayesian paradigm appropriate. We have modified the standard beta-binomial model for small areas to incorporate the prior information on the linear combination of the probabilities, which we call a constraint. Thus, there are three cases. The practitioner (a) does not specify a constraint, (b) specifies a constraint and the parameter completely, and (c) specifies a constraint and information which can be used to construct a prior distribution for the parameter. The griddy Gibbs sampler is used to fit the models. To illustrate our method, we use an example on obesity of children in the National Health and Nutrition Examination Survey in which the small areas are formed by crossing school (middle, high), ethnicity (white, black, Mexican) and gender (male, female). We use a simulation study to assess some of the statistical features of our method. We have shown that the gain in precision beyond (a) is in the order with (b) larger than (c).

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# Web survey bibliography - Survey Methodology (15)

- Does the first impression count? Examining the effect of the welcome screen design on the response rate...; 2013; Haer, R., Meidert, N.
- Optimizing quality of response through adaptive survey designs; 2013; Schouten, B., Calinescu, M., Luiten, A.
- Survey Quality; 2012; Lyberg, L. E.
- Why one should incorporate the design weights when adjusting for unit nonresponse using response homogeneity...; 2012; Kott, P. S.
- Innovations in survey sampling design: Discussion of three contributions presented at the U.S. Census...; 2011; Opsomer, J.
- A Bayesian analysis of small area probabilities under a constraint; 2011; Nandram, B., Sayit, H.
- Adaptive network and spatial sampling; 2011; Thompson, S. K.
- Nonsampling errors in dual frame telephone surveys ; 2011; Brick, J. M., Flores Cervantes, I., Lee, S., Norman, G.
- The multidimensional integral business survey response model; 2010; Bavdaz, M.
- Statistical foundations of cell-phone surveys; 2010; Wolter, K., Smith, P., Blumberg, S. J.
- Indicators for the representativeness of survey response; 2009; Schouten, B., Cobben, F., Bethlehem, J.
- Respondent Incentives in a Multi-Mode Panel Survey: Cumulative Effects on Non-Response and Bias; 2008; Jaeckle, A., Lynn, P.
- Methodology in Our Madness; 2007; Lynn, P.
- Does weighting for nonresponse increase the variance of survey means?; 2005; Little, R. J., Vartivarian, S.
- Understanding the question-answer process; 2004; Bradburn, N. M.