Internet Surveys: Can Statistical Adjustments Eliminate Coverage Bias?
Keywords: Internet penetration, undercoverage, calibration estimation, poststratification, US Behavioral Risk Factor Surveillance Survey (BRFSS)
AbstractThe Internet is an attractive mode of data collection to survey researchers due to cost savings and timeliness in comparison with other modes. However, survey estimates are subject to coverage bias if sampled persons with Internet access are systematically different from those without Internet access who were excluded from the survey. Statistical adjustments, either through weighting or modeling methods, can minimize or even eliminate bias due to non-coverage. In the current paper, we examine the coverage bias associated with conducting a hypothetical Internet survey on frame of persons obtained through a random-digit-dial (RDD) sample. We compare estimates collected during telephone interviews from households with and without Internet access using data from the 2003 Michigan Behavioral Risk Factor Surveillance System in the United States. Statistical models are developed such that the coverage bias is negligible for most of the health outcomes analyzed from the Michigan survey. Though not definitive, the analysis results suggest that statistical adjustments can reduce, if not eliminate, coverage bias in the situation we study.
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