On Bias Adjustments for Web Surveys

Web surveys exclude the entire non-internet population and often have low response rates. Therefore, statistical inference based on Web survey samples will require availability of additional information about the non-covered population, careful choice of survey methods to account for potential biases, and caution with interpretation and generalization of the results to a target population. In this paper, we focus on non-coverage bias, and explore the use of weighted estimators and hot-deck imputation estimators for bias adjustment under the ideal scenario where covariate information was obtained for a simple random sample of individuals from the non-covered population. We illustrate empirically the performance of the proposed estimators under this scenario. Possible extensions of these approaches to more realistic scenarios are discussed.