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Inferences concerning a common intraclass correlation coefficient.

A Donner, S Bull

    Biometrics
    |September 1, 1983
    PubMed
    Summary
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    This study introduces a method to estimate common intraclass correlation (rho) in two independent samples from multivariate normal populations. It also provides a statistic to test if the intraclass correlation is the same across both samples.

    Area of Science:

    • Statistics
    • Multivariate Analysis
    • Biometry

    Background:

    • Intraclass correlation (rho) measures agreement within groups.
    • Estimating common rho is crucial for comparing reliability or agreement across independent studies.
    • Existing methods may have limitations when dealing with multivariate normal data.

    Purpose of the Study:

    • To derive the maximum likelihood estimator (rhoM) for a common intraclass correlation (rho) in two independent multivariate normal samples.
    • To develop a likelihood ratio statistic for testing the equality of intraclass correlations between these two samples.
    • To compare the proposed method with existing procedures, such as those based on Fisher's transformation.

    Main Methods:

    • Maximum Likelihood Estimation (MLE) applied to two independent multivariate normal populations.

    Related Experiment Videos

  • Derivation of a likelihood ratio test (LRT) statistic.
  • Comparative analysis with Fisher's transformation method.

    • Main Results:

    • The maximum likelihood estimator (rhoM) for common intraclass correlation (rho) is successfully derived.
    • A likelihood ratio statistic is developed for testing the assumption of a common rho.
    • The performance and characteristics of the proposed estimator and test are demonstrated through examples and comparisons.

    Conclusions:

    • The derived maximum likelihood estimator provides a robust method for estimating common intraclass correlation.
    • The likelihood ratio statistic offers a formal test for the equality of intraclass correlations across independent samples.
    • The proposed approach is a valuable addition to statistical methods for analyzing correlated data in multivariate settings.