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Accurate intraclass correlation coefficient (ICC) estimates are vital for statistical analyses. A specific ICC variance formula using a normalizing transformation best recovers population ICC values in meta-analyses of clustered data.

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Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Intraclass correlation coefficient (ICC) estimates are crucial for statistical methods involving clustered data.
  • Accurate ICC values are needed for power analyses, meta-analytic effect size adjustments, and reliability measures.
  • ICC estimates are also used as primary outcomes in meta-analyses to pool agreement or reliability.

Purpose of the Study:

  • To evaluate the accuracy of meta-analytically pooled ICC estimates.
  • To compare different ICC variance formulas used as inverse variance weights in meta-analysis.
  • To identify the best performing ICC variance formula for recovering population ICC parameters.

Main Methods:

  • Meta-analysis simulation study.
  • Evaluation of pooled ICC estimates derived from various ICC variance formulas.
  • Comparison of recovered ICC values against population ICC parameters under different conditions.

Main Results:

  • The variance formula employing a normalizing transformation demonstrated superior performance.
  • This formula consistently recovered population ICC parameter values more accurately across most simulated conditions.
  • Other ICC variance formulas showed varying degrees of bias and inaccuracy.

Conclusions:

  • The choice of ICC variance formula significantly impacts the accuracy of meta-analytically pooled ICC estimates.
  • Using the normalizing transformation variance formula is recommended for more reliable meta-analytic pooling of ICCs.
  • Accurate ICC estimation is essential for valid secondary analyses and meta-analyses focusing on agreement or reliability.