Sampling for Web Surveys

Abstract

Web surveys are frequently based on samples drawn from panels with large amounts of nonresponse or haphazard selection. The availability of large-scale consumer and voter databases provides large amounts of auxilliary information for both panelists and population members. Sample matching, where a conventional random sample is selected from a population frame and the clos- est matching respondent from the panel is selected for interviewing, is proposed. It is shown that under suitable assumptions (primarily ignorability of panel membership conditional upon the match- ing variables), the resulting survey estimates are consistent with an asymptotic normal distribution. Simulation results show that the matched sample estimators are superior to weighting a random sub- sample from the panel and have a similar sampling distribution to simple random sampling from the population. In an example involving the 2006 U.S. Congressional elections, estimates using sample matching from an opt-in Web panel outperformed estimates based on phone interviews with RDD samples.