Inter-rater reliability

Laura De Pretto

Inter-rater reliability

Laura De Pretto

School of Social Sciences

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary › peer-review

Abstract

Inter-rater reliability is a crucial methodological concern across various research fields, ensuring that observed differences reflect true variation rather than inconsistencies in judgment. This article explores conceptual foundations, statistical methods, and practical strategies for enhancing inter-rater reliability. Advances in AI, machine learning, and Bayesian modeling offer new opportunities but raise ethical challenges related to transparency and bias. Practical solutions, including structured rater training, calibration techniques, and hybrid human-AI approaches, are examined. Emphasizing interdisciplinary collaboration, methodological rigor, and continuous innovation, this article highlights the necessity of maintaining reliability as a cornerstone of scientific inquiry.

Original language	English
Title of host publication	Encyclopedia of measurement in social sciences
Editors	Ajita Srivastava, Klaus Boehnke
Publisher	Elsevier
Chapter	89
Edition	2nd
Publication status	Accepted/In press - Jan 2026

Keywords

Agreement, Bayesian modeling, Big data analytics, Cognitive bias, Crowdsourcing, Interdisciplinary collaboration, Inter-rater reliability, Machine learning, Measurement validity, Methodological rigor, Natural Language Processing (NLP), Psychometrics, Reliability statistics, Transparency, Weighted kappa

Cite this

@inbook{5d7345b4d9d84a9789ad58d86f961c2c,

title = "Inter-rater reliability",

abstract = "Inter-rater reliability is a crucial methodological concern across various research fields, ensuring that observed differences reflect true variation rather than inconsistencies in judgment. This article explores conceptual foundations, statistical methods, and practical strategies for enhancing inter-rater reliability. Advances in AI, machine learning, and Bayesian modeling offer new opportunities but raise ethical challenges related to transparency and bias. Practical solutions, including structured rater training, calibration techniques, and hybrid human-AI approaches, are examined. Emphasizing interdisciplinary collaboration, methodological rigor, and continuous innovation, this article highlights the necessity of maintaining reliability as a cornerstone of scientific inquiry.",

keywords = "Agreement, Bayesian modeling, Big data analytics, Cognitive bias, Crowdsourcing, Interdisciplinary collaboration, Inter-rater reliability, Machine learning, Measurement validity, Methodological rigor, Natural Language Processing (NLP), Psychometrics, Reliability statistics, Transparency, Weighted kappa",

author = "\{De Pretto\}, Laura",

year = "2026",

month = jan,

language = "English",

editor = "Ajita Srivastava and Klaus Boehnke",

booktitle = "Encyclopedia of measurement in social sciences",

publisher = "Elsevier",

address = "Netherlands",

edition = "2nd",

}

TY - CHAP

T1 - Inter-rater reliability

AU - De Pretto, Laura

PY - 2026/1

Y1 - 2026/1

N2 - Inter-rater reliability is a crucial methodological concern across various research fields, ensuring that observed differences reflect true variation rather than inconsistencies in judgment. This article explores conceptual foundations, statistical methods, and practical strategies for enhancing inter-rater reliability. Advances in AI, machine learning, and Bayesian modeling offer new opportunities but raise ethical challenges related to transparency and bias. Practical solutions, including structured rater training, calibration techniques, and hybrid human-AI approaches, are examined. Emphasizing interdisciplinary collaboration, methodological rigor, and continuous innovation, this article highlights the necessity of maintaining reliability as a cornerstone of scientific inquiry.

AB - Inter-rater reliability is a crucial methodological concern across various research fields, ensuring that observed differences reflect true variation rather than inconsistencies in judgment. This article explores conceptual foundations, statistical methods, and practical strategies for enhancing inter-rater reliability. Advances in AI, machine learning, and Bayesian modeling offer new opportunities but raise ethical challenges related to transparency and bias. Practical solutions, including structured rater training, calibration techniques, and hybrid human-AI approaches, are examined. Emphasizing interdisciplinary collaboration, methodological rigor, and continuous innovation, this article highlights the necessity of maintaining reliability as a cornerstone of scientific inquiry.

KW - Agreement, Bayesian modeling, Big data analytics, Cognitive bias, Crowdsourcing, Interdisciplinary collaboration, Inter-rater reliability, Machine learning, Measurement validity, Methodological rigor, Natural Language Processing (NLP), Psychometrics, Reli

M3 - Entry for encyclopedia/dictionary

BT - Encyclopedia of measurement in social sciences

A2 - Srivastava, Ajita

A2 - Boehnke, Klaus

PB - Elsevier

ER -

Inter-rater reliability

Abstract

Keywords

Fingerprint

Cite this