E-mail and World Wide Web surveys have been the subject of much hyperbole about their capabilities as well as some criticism of their limitations. In this report, the authors examine what is known and not known about the use of the Internet for surveying. Specifically, they consider evidence in the literature regarding response rates, timeliness, data quality, and cost. Using this evidence, the authors evaluate popular claims that Internet-based surveys can be conducted more quickly, effectively, cheaply, and/or easily than surveys conducted via conventional modes. They find that the realities of cost and speed often do not live up to the hype. Nonetheless, it is possible to implement Internet-based surveys in ways that are effective and cost-efficient. The authors conclude that the Internet will continue to grow in importance for conducting certain types of research surveys

This chapter is a comprehensive overview of sampling methods for web and e-mail ('Internetbased') surveys. It reviews the various types of sampling method -both probability and nonprobability -and examines their applicability to Internet-based surveys. Issues related to Internetbased survey sampling are discussed, including difficulties assembling sampling frames for probability sampling, coverage issues, and nonresponse and selection bias. The implications of the various survey mode choices on statistical inference and analyses are summarized.

The significant reductions in noise and light events resulting from the intervention did not lead to significant improvements in the day sleep and most night sleep measures. An intervention that combines both behavioral and environmental strategies and that addresses daytime behavioral factors associated with poor sleep (e.g., excessive time in bed) would potentially be more effective in improving the night sleep and quality of life of nursing home residents.

This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed significantly better than the EARS' methods across all of the scenarios we evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various types and levels of random daily variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.

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