The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Intraclass correlations: Uses in assessing rater reliability.
Abstract
Reliability coefficients often take the form of intraclass correlation coefficients. In this article, guidelines are given for choosing among six different forms of the intraclass correlation for reliability studies in which n target are rated by k judges. Relevant to the choice of the coefficient are the appropriate statistical model for the reliability and the application to be made of the reliability results. Confidence intervals for each of the forms are reviewed.
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In the citing paper
“As a result, the inter-rater agreement was quite high for categorical variables (mean Cohen's kappa = 0.73, SD = 0.13) and for continuous variables (mean intraclass correlation [26] = 0.80, SD = .14).”
From Social Relationships and Mortality Risk: A Meta-analytic Review (2010) · cites this paper as [26]
In the citing paper
“we used the methodology Shrout and Fleiss (1979) describe and computed the Shrout-Fleiss single ICC score agreement of .998, which is considered quite high (Koo and Li 2016).”
From Driving Brand Engagement Through Online Social Influencers: An Empirical Investigation of Sponsored Blogging Campaigns (2019) · cites this paper as Shrout and Fleiss (1979)
In the citing paper
“In contrast, Cohen's quadratically weighted kappa converges to two-factor intraclass correlation coefficients with random and fixed raters (i.e., the so-called ICC (2,1) and ICC(3,1) coefficients), where the items and raters are the two factors (Carrasco, 2010;Fleiss & Cohen, 1973;Janson & Olsson, 2001;Schuster, 2004;Schuster & Smith, 2005;Shrout & Fleiss, 1979;Warrens, 2014).”
From Quadratically Weighted Agreement Coefficients: Interpretations and Connections (2026) · cites this paper as Shrout & Fleiss, 1979
In the citing paper
“A sample of 11% of video data was double coded by the researcher and an experienced coder (the developer of CHIPPA). Inter‐rater reliability was based on intra‐class correlation (ICC) statistics from the mean rating for the two coders (k = 2) using a two‐way mixed effects consistency model (see Table 2 below) and resulted in good to excellent reliability across the CHIPPA subscales (Koo & Li, 2016”
From A plausible role of imagination in pretend play, counterfactual reasoning, and executive functions (2023) · cites this paper as Shrout & Fleiss, 1979
In the citing paper
“The results suggest that coders showed high agreement (intraclass correlation [2, 1] = 0.85; Shrout & Fleiss, 1979).”
From Respiratory and cognitive mediators of treatment change in panic disorder: Evidence for intervention specificity. (2010) · cites this paper as Shrout & Fleiss, 1979
In the citing paper
“Interrater reliability of the pre- and posttreatment assessments was excellent (intraclass correlations [2, 1] = 0.97 and 0.96, respectively; Shrout & Fleiss, 1979).”
From Respiratory and cognitive mediators of treatment change in panic disorder: Evidence for intervention specificity. (2010) · cites this paper as Shrout & Fleiss, 1979
In the citing paper
“ICC decomposes observed variance into between-query variance (task difficulty) and within-query variance (agent inconsistency), highlighting whether reported results reflect true capability or measurement noise.”
From Stochasticity in Agentic Evaluations: Quantifying Inconsistency with Intraclass Correlation (2025) · cites this paper as Shrout and Fleiss 1979
In the citing paper
“The ICC quantifies the proportion of total variance attributable to differences between tasks and can be understood as a function of both the difficulty of the dataset (variance between tasks) and the consistency of the agent (variance within tasks).”
From Stochasticity in Agentic Evaluations: Quantifying Inconsistency with Intraclass Correlation (2025) · cites this paper as Shrout and Fleiss 1979
In the citing paper
“Reproducibility of the final placenta, fetus and amniotic fluid volume in millilitres was assessed using the ICC (2,1) [37], [38] for inter-observer repeatability between the semi-automated Random Walker results, regarded as the ground-truth used for performance evaluation, and the newly generated FCNN outputs.”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [37]
Citations
Cites
Coefficient Alpha and the Internal Structure of Tests
Cronbach, L. J. (1951) · Psychometrika · doi:10.1007/bf02310555 · Cited by 43,931
Statistical principles in experimental design.
Winer, B. J. (1962) · McGraw-Hill Book Company · doi:10.1037/11774-000 · Cited by 27,137
Rozeboom, W. W., Lord, F. M., Novick, M. R., & Birnbaum, A. (1969) · American Educational Research Journal · doi:10.3102/00028312006001112 · Cited by 8,013
Rozeboom, W. W., Lord, F. M., Novick, M. R., & Birnbaum, A. (1969) · American Educational Research Journal · doi:10.2307/1162101 · Cited by 8,012
Snedecor, G. W., & Cochran, W. G. (1967) · Cited by 6,573
Scheffé, H. (1960) · Soil Science · doi:10.1097/00010694-196006000-00016 · Cited by 5,625
An Approximate Distribution of Estimates of Variance Components
Satterthwaite, F. E. (1946) · Biometrics Bulletin · doi:10.2307/3002019 · Cited by 4,373
The Intraclass Correlation Coefficient as a Measure of Reliability
Bartko, J. (1966) · Psychological Reports · doi:10.2466/pr0.1966.19.1.3 · Cited by 2,206
Statistics for the social sciences
Hays, W. L. (1973) · Medical Entomology and Zoology · Cited by 2,081
The theory of the estimation of test reliability
Kuder, G. F., & Richardson, M. W. (1937) · Psychometrika · doi:10.1007/bf02288391 · Cited by 1,953
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Metadata
- Authors
- Shrout, P. E., & Fleiss, J. L.
- Affiliations
- Columbia University (US)
- ORCID
- Shrout, Patrick E.
- Venue
- Psychological Bulletin
- Published
- 1979
- Volume
- 86
- Issue
- 2
- Pages
- 420-428
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2141403362
- Cited by
- 24,532
- References
- 20
- Citations by year
- 2026: 1452 · 2025: 1085 · 2024: 998 · 2023: 1023 · 2022: 1083 · 2021: 1379 · 2020: 1189 · 2019: 1116 · 2018: 1178 · 2017: 1113 · 2016: 1106 · 2015: 1057 · 2014: 1172 · 2013: 1044 · 2012: 914
- FWCI
- 13.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Multidisciplinary Science and Engineering Research
- Keywords
- intraclass correlation coefficient, rater reliability, inter-rater reliability, reliability coefficients, confidence intervals, multiple raters
- MeSH terms
- Humans, Reproducibility of Results, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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