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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Cited by
Sorted by citations
Items 23,301–23,400 of 23,883
Junghaenel, D., Schneider, S., Stone, A., Orriens, B., Gutsche, T., Darling, J., Perez-Arce, F., Hayden, O., & Kapteyn, A. (2026) · Quality of Life Research · doi:10.1007/s11136-026-04279-9
Natural language processing for non-violent conflict detection in institutional discourse
Cerise, S., Nava, C. R., & Tedeschi, S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133182
Development and initial validation of the digital health equity questionnaire for rural older adults
Liu, Q., Aifeire, A., Meng, N., Huang, M., Maimaitiyili, D., Sun, H., Kang, L., & You, S. (2026) · BMC Public Health · doi:10.1186/s12889-026-28085-7
Li, R., & Xu, X. (2026) · Library & Information Science Research · doi:10.1016/j.lisr.2026.101415 · Cited by 1
Improving LLM-Assisted Domain-Specific Design Tasks Through Domain-Structured Persona Prompting
Zhao, S., & Ji, D. (2026) · Applied Sciences · doi:10.3390/app16125770 · Cited by 1
Rahul, Singh, V., Gowda, A. K. S., et al. (2026) · Indian Journal of Orthopaedics · doi:10.1007/s43465-026-01846-7
Chen, K., Husain, A., Rapoport, D. M., & Wu, H. (2026) · Sleep Medicine · doi:10.1016/j.sleep.2026.109075
Alves-Maciel, B., Pampati, S., Fodeman, A., Handel, M. M. V., Zheng, L., Ye, Z., Yaesoubi, R., & Salomon, J. A. (2026) · PLoS ONE · doi:10.1371/journal.pone.0349759
Arora, K., & Maruthy, S. (2026) · Hearing Research · doi:10.1016/j.heares.2026.109706
Cabitza, F., & Vicente, L. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103855 · Cited by 2
Kibar, P. N. (2026) · Educational Media International · doi:10.1080/09523987.2026.2683335
Fathima, F., & Nosenoor, M. (2026) · The Indian Journal of Medical Research · doi:10.25259/ijmr_2728_2025
Bumın, G., Gündoğmuş, E., Tennant, A., et al. (2026) · Rheumatology Advances in Practice · doi:10.1093/rap/rkag066
Beigneux, Y., Gitton, C., Anwar, A. R., et al. (2026) · Brain Communications · doi:10.1093/braincomms/fcag218
Business Model Coherence and Configuration Drift: A Longitudinal Panel Test Across the Firm LifespanPreprint
Pongeluppi, M., & Gudmundsson, S. V. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6916093
Kim, M. J., Eom, S., Kim, J., Park, S.-J., Park, K., On, Y. K., Joo, S., Chang, M., Na, Y., Park, M., et al. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag086
How Accurate are Our Near Reading Charts? An Assessment of 19 Charts Against ISO Standards
Murphy, T. I., Chen, J., & Leung, M. (2026) · Ophthalmic and Physiological Optics · doi:10.1007/s44402-026-00123-2
Belbouli, A., Touhami, F., Elmoukhtar, M., Mouhtat, I., El-Bir, A., & Ketatni, H. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026694
Sjödin, C., Oras, J., Johansson, L., Persson, P., Olausson, S., Holmqvist, J., Ille, A., Vidovic', N., & Svensson, C. J. (2026) · Acta Anaesthesiologica Scandinavica · doi:10.1111/aas.70280
Carter, L., Surti, T., Abildskov, T. J., Shum, E., Hertzler, A., Dodda, M., Rodriguez, A., Brown, C., Sanchez, J. R., Tar, D., et al. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100361
Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study
Preiksaitis, C. M., Alvarez, A., Winkel, M., Karamatsu, M., Brown, I. M., Sama, N., Morris, L., Lee, J., Gubbels, A., Wahl, E., et al. (2026) · JMIR AI · doi:10.2196/92193
Multi-threshold volumetric 18F-FET PET for tumor detection and prognosis in primary glioma
Oo, T. T., Grega, S. D., Smith, N. J., et al. (2026) · Neuro-Oncology Advances · doi:10.1093/noajnl/vdag159
CoStage: An Embodied AI Co-Creation System for Children’s Performative Storytelling with Robots
Song, J., Wan, H., Han, L., Gao, Y., Yip, D. K. M., & Tong, X. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3813101
CFOs Meet LLMsPreprint
Graham, J. R., Harvey, C. R., & Jha, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6920881
Al-Juhani, A. A., Binshalhoub, A. A., Aljohani, N., Aljohani, O. O., Alasmari, A. F., & Badghaish, M. K. (2026) · The Saudi Journal of Forensic Medicine and Sciences · doi:10.4103/sjfms.sjfms_12_25
Dajić, I., Sauerzopf, U., Weidenauer, A., Diendorfer, C., Landesmann, K., Rausch, I., Hahn, A., Spurny-Dworak, B., Klöbl, M., Reed, M., et al. (2026) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-026-07998-w
Kostin, A., Saevskiy, A., Alam, M. A., Jiang, Y., Suntsova, N., & Alam, M. N. (2026) · Cells · doi:10.3390/cells15121075
Boverhof, B.-J., Takatzoglou, N., Redekop, K., Groot, C. U.-D., Visser, J. J., & Mölken, M. R. (2026) · Value in Health · doi:10.1016/j.jval.2026.05.009
An OODA Phase-Segmented Framework for Interpretable Driver State Change Detection
Lu, Y., Mu, Y., Xue, P., et al. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426500308
I or we? How self-categorization shapes decision-making in team sports
Guyomarch, A., Cottereau, B. R., Martin, S., Pellet, J., Pété, É., & Campo, M. (2026) · Team Sports Studies · doi:10.1016/j.tss.2026.100008
Sia, M. Y., Lu, C., Tzeng, O. J. L., & Wang, S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351177
Heatley, A. C., Enbiyaoglu, M., Engelmann, J., Zhou, Y., Cortina‐Borja, M., Khawaja, A. P., Denniston, A. K., Zhu, L. Z., Keane, P. A., & Wagner, S. K. (2026) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.67.6.24
Simon, V., Zitzmann, S., Vitinius, F., & Ramsauer, B. S. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08258-9
Kawaguchi, A., Kawaguchi, W., Hayashi, M., Yasuda, K., & Ohashi, Y. (2026) · Radiological Physics and Technology · doi:10.1007/s12194-026-01084-4
Analyzing public library outcomes using the logic model framework
Na, M. K., Kim, G. Y., Oh, J. E., & Lee, J. Y. (2026) · Performance Measurement and Metrics · doi:10.1108/pmm-03-2026-0021
Biswal, S., Sagar, P., Just, M. D., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-58318-z
Luo, Y., & Li, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6958664
Jannati, A., Toro‐Serey, C., Ciesla, M., Chen, E., Bates, D., Showalter, J., Tobyne, S. M., & Pascual‐Leone, Á. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1806000 · Cited by 1
Marcus, J. C., Akin, C., Zhang, J., Easton, R., Lim-Watson, M. Z., Siebenhaar, F., Gwaltney, C. J., & Turner, R. (2026) · Quality of Life Research · doi:10.1007/s11136-026-04291-z
Taghiparast, A. H., Mahmoudian, A., Ghaemi‐Amiri, M., & Ghaffari, F. (2026) · Medical Education Online · doi:10.1080/10872981.2026.2690326
Pimentel, R. P. F. D. A., & Meyer, G. J. (2026) · Assessment · doi:10.1177/10731911261455137
Augustine, J. J., Mason, R., Lewis, T. J., Imler, M. H., Kittelman, A., & Beversdorf, D. Q. (2026) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-026-07388-z
Fu, R., Wang, L., Wang, N., et al. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70359
Du, Y.-M., Lu, J., Zhang, H., et al. (2026) · Health and Quality of Life Outcomes · doi:10.1186/s12955-026-02567-z
Pilotní studie využitelnosti nástroje CLASS Pre-K v českých mateřských školách
Koželuh, O., Koželuhová, E., & Greger, D. (2026) · ORBIS SCHOLAE · doi:10.14712/23363177.2026.2
Moll, L., Rießen, R., Dahlmann, P., & Häske, D. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09717-3 · Cited by 1
Knowledge-graph anchoring enables safer large language model reasoning for rare-tumor managementPreprint
Chu, Y., Hua, C., Qing, H., Menge, F., Reissfelder, C., Kasper, B., Xu, Y., Jakob, J., Li, C., & Yang, C. (2026) · Research Square · doi:10.21203/rs.3.rs-10232500/v1
Splenic FDG PET uptake and CT volume as prognostic biomarkers in diffuse large B cell lymphoma
D’Argenzio, J., Rampi, N., Pozzi, J., Vespro, V., Pavan, M., Casiraghi, E., Rossi, F. G., Castello, A., Forzenigo, L. V., Castellani, M., et al. (2026) · La radiologia medica · doi:10.1007/s11547-026-02233-w
Faraz, S. A. F., Ahmed, I., & Zaidi, S. J. A. (2026) · BMC Oral Health · doi:10.1186/s12903-026-08994-0
Olmos, A. A., Lawson, D. J., Sontag, S. A., Richardson, L. D., Roth, B. L., Jeon, S., Redinger, A. L., Gamache, S., & Trevino, M. A. (2026) · Experimental Brain Research · doi:10.1007/s00221-026-07334-6
Cazón, L. I., Paredes, J. A., Quiroga, M., et al. (2026) · Tropical Plant Pathology · doi:10.1007/s40858-026-00818-4
Kılavuz, M., Ağralı, C., & Kanbay, Y. (2026) · BMC Public Health · doi:10.1186/s12889-026-28212-4
Tozuka, K., Sasaki, G., & SAKAGUCHI, M. (2026) · IEEE Transactions on Haptics · doi:10.1109/toh.2026.3705210
Design and Validation of the Moral Courage in Bullying Scale in Adolescents
Bravo, A., García-Carrera, P., Ruiz, R. O., et al. (2026) · Journal of Interpersonal Violence · doi:10.1177/08862605261451861
Aranda, E. M., Villanueva, C., Austria, A., & Reyes, M. C. (2026) · International Journal of Research Studies in Education · doi:10.5861/ijrse.2026.26869
Deep Learning Applications in Myocardial Perfusion SPECT Imaging: A Systematic Review
Khodakarami, M., & Mofrad, F. B. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10698-1
Digital terroir and ecosystem service auditability via machine learning in geographical indications
Oliveira, C. V. D. S., Santos, L. D. V., Quirino, G. D. S., Abud, A. K. D. S., Holanda, F. S. R., Filho, R. N. D. A., & Azevedo, T. C. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08944-z
The Need for Development of Power Prediction Models in Modern Dance Performance
Agopyan, A., & Tekin, D. (2026) · Journal of Dance Medicine & Science · doi:10.1177/1089313x261461412
Reliability and standard error of measurement of pressure pain thresholds at articular landmarks
Tomschi, F., & Hilberg, T. (2026) · British Journal of Pain · doi:10.1177/20494637261463704
Thoracic limb stride length is associated with cognitive impairment in aging dogs
Baigi, S. R., Stywall, A., Yang, C. C., Mondino, A., Fefer, G., Panek, W. K., Simon, K. E., Case, B. C., Gruen, M. E., & Olby, N. J. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1814017
Crasta, D., Stipancic, K. L., Anand, S., et al. (2026) · Journal of Speech Language and Hearing Research · doi:10.1044/2026_jslhr-25-00496
Maleček, J., Malíř, R., Mašek, D., et al. (2026) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000005592
Vibe Checks to Visible Criteria: AI-Assisted Interview Calibration in Postgraduate Admissions
Yong, V. J. S., & Braud, T. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29791-4_22
Nedelcu, A., Russe, M. F., Wilpert, C., Oerther, B., Nickel, D. M., Strecker, R., Bamberg, F., Weiß, J., & Engel, H. (2026) · European Radiology Experimental · doi:10.1186/s41747-026-00758-3
Mapping Fatty Acid Composition in the Human Knee: Short‐Term Repeatability at 3T
Martel, D., Adlung, A., Busi, B., Bernadin, R., Shah, Y., Kirsch, T., Kijowski, R., Madelin, G., & Ruiz, A. (2026) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70396
Nakao, M., Yokota, K., Tarukado, K., et al. (2026) · Spinal Cord · doi:10.1038/s41393-026-01234-w
Accuracy and agreement of play the ball coding in rugby league
McIntosh, B., Rennie, M. J., Novak, A. R., et al. (2026) · International Journal of Sports Science & Coaching · doi:10.1177/17479541261461381
Sun, X. R., Wang, Y., Wang, L., et al. (2026) · Journal of Vestibular Research · doi:10.1177/09574271261465716
Mazhari-Jensen, D. S., Mørch, C. D., Davies, P. L., et al. (2026) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2026.112281
Multi-Agent LLMs for Occupational Profiling: Psychometric Validation on 1636 Chinese Occupations
Han, Y., Luo, X., Ji, F., & Kong, X. (2026) · Behavioral Sciences · doi:10.3390/bs16071064
Safran, E. E., Çavuşoğlu, I., İldeniz, Y., & Alaca, N. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15135017
Borgert, N., Ulmann, T., & Merchel, R. (2026) · doi:10.1109/spw72489.2026.00032
Kaya, N., Demircan, S. E., Yavuz, M. Z., et al. (2026) · APOS Trends in Orthodontics · doi:10.25259/apos_51_2026
Amir‐Behghadami, M., & Ghanbari-Homaie, S. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-116434
Anatomy-Guided Vision Transformer for Multitask Thermal Gait Analysis in the Timed Up-and-Go Test
Chen, W., Hsieh, C.-Y., Liu, K.-C., Peng, S.-Y., Wei, H., & Tsao, Y. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3706259
GOBench: Benchmarking and instruction-tuned assessment of geometric optics in multimodal LLMs
Zhu, X., Jia, Z., Zhang, Z., & Zhai, G. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104885
Katayama, K., Nakamichi, M., & Yamada, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-52385-y · Cited by 1
Gharaibeh, M., Ayasrah, M. N., Khatib, A. J. A., et al. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14067-0
Žabková, M., Čížková, M., Šlipogor, V., Vodička, R., & Konečná, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.25.734437
Moradi, K., Peters, B. E., Dillman, L., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351180
Liu, Y., Wong, T., Chau, T. W., & Cao, Y. (2026) · Education Sciences · doi:10.3390/educsci16070994
Large Language models for banking supervision: reliability evidence from European systemic banks
García-Llorente, C., & Martos, J. I. O. (2026) · Finance research letters · doi:10.1016/j.frl.2026.110440
Morada, A. O., Becker, J. L., Furey, M. J., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10029588/v1
Eisenberg, M. J., van Horn, J. E., van der Put, C. E., et al. (2026) · Criminal Justice and Behavior · doi:10.1177/00938548261455730
Alqarni, A. M., & Othman, R. (2026) · Journal of Back and Musculoskeletal Rehabilitation · doi:10.1177/10538127261466486
Alghamdi, S. A., Alrasheed, A., Kariri, A., et al. (2026) · Healthcare · doi:10.3390/healthcare14131917
Owens, J. S., Evans, S. W., Allan, N. P., Dillon, C., Himawan, L. K., Medina, D., Shea, M., & Vitucci, D. (2026) · Journal of School Psychology · doi:10.1016/j.jsp.2026.101585 · Cited by 1
Inter rater reliability of practical assessment criteria for aquatic literacy in children
Staub, I., Brinkschulte, M., Costa, A. M., et al. (2026) · Discover Education · doi:10.1007/s44217-026-01842-x
Kawamura, K., Etoh, S., 真, 上., Ohama, R., Hoei, T., Hagihara, S., Enohata, K., & Shimodozono, M. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-02084-6
Aykent, B. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261462281
Morisot, C., Khazraji, I. C., Habouzit, V., Magand, N., Roy, B. L., Boukili, I. E., Boutet, C., Grange, S., & Grange, R. (2026) · CardioVascular and Interventional Radiology · doi:10.1007/s00270-026-04528-x
Ng, J. C. Y., Armani, N., Sun, X., et al. (2026) · European Spine Journal · doi:10.1007/s00586-026-10146-5
Ran, F.-Q., van de Weijer, J., & Zheng, Z.-Y. (2026) · Brain Science · doi:10.3390/brainsci16070722
Liu, X., Yuan, J., Yang, Q., Fan, Y., 修文娟, & Shen, Y. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1805304
Verbeelen, K., Gürşen, C., Aperen, K. V., et al. (2026) · Clinical Physiology and Functional Imaging · doi:10.1111/cpf.70078
Kallionpää, S., Granö, N., Ranta, K., et al. (2026) · Psychosis · doi:10.1080/17522439.2026.2696279
Istanbullu, O. B., & Yasan, Ö. B. (2026) · Methods · doi:10.1016/j.ymeth.2026.07.004
Ünlü, R., Yilmaz, B., Usta, H. E., et al. (2026) · Tomography · doi:10.3390/tomography12070101
Ecosystem-Driven Design Thinking
Lazo-Barreda, D., Machaca, E. S. M., Paredes, K. R., Ancco, M., & Cuya-Zevallos, C. (2026) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v16i4.61279
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
