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Cited by 2,584Animal Behavior and ReproductionReliability and Agreement in MeasurementMorphological variations and asymmetry

The DOI matches its Crossref record; 84 of the 85 references it cites were found in a registry or an index.

Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

Nakagawa, Shinichi, Schielzeth, Holger·2010·Biological Reviews
DOI 10.1111/j.1469-185x.2010.00141.x
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Abstract

Repeatability (more precisely the common measure of repeatability, the intra-class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between-subject (or between-group) variation. As a consequence, the non-repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non-Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non-Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation-based, analysis of variance (ANOVA)-based and linear mixed-effects model (LMM)-based methods, while for non-Gaussian data, we focus on generalised linear mixed-effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM- and GLMM-based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow-sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non-Gaussian data.

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Citations

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Research works printed but not linked: 3

  1. Becker W. A.(1992).A manual of quantitative genetics 5th edition. Academic Enterprises Pullman WA.
  2. Faraway J. J., 2006, Extending the linear model
  3. Snijders T. A. B., 1999, Multilevel analysis: an introduction to basic and advanced multilevel modeling.

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Metadata

Authors
Nakagawa, S., & Schielzeth, H.
Affiliations
Max Planck Society (DE)
Corresponding author
  • Nakagawa, Shinichi
  • Schielzeth, Holger
ORCID
Venue
Biological Reviews
Publisher
Wiley
Published
2010-06-21
Volume
85
Issue
4
Pages
935-956
ISSN
  • 0006-3231 (ISSN-L)
  • 1464-7931
  • 1469-185X
DOI
10.1111/j.1469-185x.2010.00141.x
OpenAlex
W2045443942
Cited by
2,584
References
85
Citations by year
2026: 109 · 2025: 165 · 2024: 141 · 2023: 171 · 2022: 235 · 2021: 222 · 2020: 206 · 2019: 197 · 2018: 215 · 2017: 198 · 2016: 183 · 2015: 167 · 2014: 136 · 2013: 107 · 2012: 78
FWCI
8.27
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Biological reviews/Biological reviews of the Cambridge Philosophical Society (publishedVersion)
  • PubMed (publishedVersion)
  • PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Morphological variations and asymmetry
Keywords
repeatability, intraclass correlation coefficient, non-Gaussian data, linear mixed models, measurement error, phenotypic flexibility, statistical significance, parametric bootstrapping, permutation tests, Bayesian approach, narrow-sense heritability, binary data, count data, proportion data
MeSH terms
Analysis of Variance, Phenotype, Regression Analysis, Statistics as Topic, Reproducibility of Results, Confidence Intervals, Normal Distribution, Linear Models, Least-Squares Analysis

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