Аннотация:List-based samples are often biased because of coverage errors. The problem is especially acute in societies where the level of internal migration is high and where record keeping on the population is not reliable. We propose a solution based on spatial sampling that overcomes the inability to reach migrants in traditional area samples based on household lists. A comparison between a traditional study and our sample of Beijing demonstrates that coverage bias is greatly reduced. The successful incorporation of mobile urban residents has important substantive effects, in both univariate and multivariate analyses of public opinion data.
