Generalized reliability based on distances

Meng Xu, Philip T. Reiss, Ivor Cribben

Research output: Contribution to journalArticlepeer-review


The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance-based ICC (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test-retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman-Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC.

Original languageEnglish
Pages (from-to)258-270
Number of pages13
Issue number1
StatePublished - Mar 2021

Bibliographical note

Publisher Copyright:
© 2020 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.


  • Spearman-Brown formula
  • functional connectivity
  • intraclass correlation coefficient
  • test-retest reliability

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability


Dive into the research topics of 'Generalized reliability based on distances'. Together they form a unique fingerprint.

Cite this