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Unpublished Paper
Generalized reliability based on distances
  • Meng Xu, University of Haifa
  • Philip T. Reiss, University of Haifa
  • Ivor Cribben, University of Alberta
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 intraclass correlation coefficient (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.
  • functional connectivity,
  • intraclass correlation coefficient,
  • Spearman-Brown formula,
  • test-retest reliability
Publication Date
Citation Information
Meng Xu, Philip T. Reiss and Ivor Cribben. "Generalized reliability based on distances" (2019)
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