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Related Concept Videos

Margin of Error01:27

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Estimating Undercoverage Bias of Internet Users.

Jason Hsia1, Guixiang Zhao2, Machell Town2

  • 1Division of Population Health, Centers for Disease Control and Prevention, 4770 Buford Hwy, NE S107-6, Atlanta, GA 30341.

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This summary is machine-generated.

Web surveys may introduce bias by excluding noninternet users. This study quantified undercoverage bias in health surveys, finding significant differences in self-reported health, smoking, and drinking behaviors between internet users and nonusers.

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Area of Science:

  • Public Health
  • Survey Methodology
  • Health Disparities

Background:

  • Declining response rates in traditional telephone surveys like the Behavioral Risk Factor Surveillance System (BRFSS) necessitate exploring alternative data collection methods.
  • Web surveys offer a potential cost-effective alternative but risk introducing coverage bias due to the exclusion of noninternet users.

Purpose of the Study:

  • To quantify the undercoverage bias associated with internet use in health-related behavioral risk factor surveillance.
  • To examine the components contributing to this bias: the proportion of noninternet users and prevalence differences between internet users and nonusers.

Main Methods:

  • Analysis of 402,578 respondents from the 2017 BRFSS, focusing on internet use, self-reported health, current smoking, and binge drinking.
  • Partitioning undercoverage bias into the proportion of noninternet users and the difference in prevalence of key health variables between internet users and noninternet users.

Main Results:

  • The weighted proportion of noninternet users was 15.0%, increasing with age and decreasing with education.
  • Significant undercoverage bias was observed: -19.2% for self-reported health, -4.0% for current smoking, and 8.4% for binge drinking.
  • Demographic subgroups exhibited substantial biases and relative biases for these variables.

Conclusions:

  • Undercoverage bias due to internet use significantly impacts estimates for key health behaviors.
  • Both the prevalence of noninternet use and the differing health profiles of users versus nonusers contribute to this bias.
  • Findings inform strategies for transitioning health surveys to more cost-effective modes while mitigating coverage bias.