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Confidence Interval for Estimating Population Mean01:25

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Bootstrap Estimate of Bias for Intraclass Correlation.

Xiaofeng Steven Liu1, Kelvin Terrell Pompey

  • 1Xiaofeng Steven Liu, Department of Educational Studies, University of South Carolina, Columbia, SC 29208, USA, xliu@email.sc.edu.

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|March 5, 2020
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Summary

Estimating intraclass correlations can be biased. Cluster bootstrapping offers a robust method to assess this bias without requiring normality assumptions, improving statistical accuracy in various study designs.

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Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Intraclass correlation estimates are prone to bias.
  • Analytical methods for bias assessment often rely on normality assumptions.
  • Limited analytical approaches exist to quantify bias in intraclass correlation estimates.

Purpose of the Study:

  • To introduce and demonstrate cluster bootstrapping for estimating bias in intraclass correlation.
  • To provide a method for bias assessment that does not rely on model assumptions.
  • To illustrate the practical application of bias estimation in a real-world dataset.

Main Methods:

  • Utilized cluster bootstrapping technique.
  • Applied the method to a well-known dataset for intraclass correlation analysis.
  • Calculated bias in intraclass correlation estimation.

Main Results:

  • Demonstrated the bias in intraclass correlation estimates using a practical example.
  • Showcased the effectiveness of cluster bootstrapping in estimating bias.
  • Highlighted the implications of bias for different study designs.

Conclusions:

  • Cluster bootstrapping is a valuable, assumption-free method for estimating bias in intraclass correlation.
  • This approach enhances the reliability of statistical findings in studies utilizing intraclass correlation.
  • The findings have broad implications for the design and interpretation of clustered data studies.