• Skip to secondary menu
  • Skip to main content
  • Skip to primary sidebar
  • My Store
  • Glossary
  • Home
  • About Me
  • Contact Me

Statistics By Jim

Making statistics intuitive

  • Graphs
  • Basics
  • Hypothesis Testing
  • Regression
  • ANOVA
  • Probability
  • Time Series
  • Fun
  • Calculators

Fleiss’ Kappa

By Jim Frost

« Back to Glossary Index

Fleiss’ kappa is a statistical measure of inter-rater reliability that assesses the agreement between three or more raters when they classify items into categories, accounting for agreement that would occur by chance. Like Cohen’s kappa, it ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates agreement no better than chance, and negative values suggest worse-than-chance agreement. Values above 0.6 are typically considered moderate to good agreement, though interpretation standards can vary depending on the field.

Fleiss’ kappa is designed for categorical (nominal) data and works with ratings made by multiple independent raters. It assumes that each item is rated by the same number of raters and that categories are mutually exclusive.

Fleiss’ kappa is designed specifically for three or more raters. For two-rater situations, consider using Cohen’s kappa. For ordinal or more complex data types, consider using Krippendorff’s alpha as an appropriate reliability measure.

For example, if five pathologists independently classify biopsy samples as benign or malignant, Fleiss’ kappa can evaluate how consistently they agree, adjusting for chance agreement. If the kappa value is 0.68, this would typically be interpreted as substantial agreement, suggesting that the pathologists’ ratings are reasonably reliable.

Related

Related Articles:
  • Inter-Rater Reliability: Definition, Examples & Assessing
  • Inter-Rater Reliability: Definition, Examples & Assessing
  • Glossary: Cohen’s Kappa
« Back to Glossary Index

Primary Sidebar

Meet Jim

I’ll help you intuitively understand statistics by focusing on concepts and using plain English so you can concentrate on understanding your results.

Read More...

Buy My Introduction to Statistics Book!

Cover of my Introduction to Statistics: An Intuitive Guide ebook.

Buy My Hypothesis Testing Book!

Cover image of my Hypothesis Testing: An Intuitive Guide ebook.

Buy My Regression Book!

Cover for my ebook, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models.

Subscribe by Email

Enter your email address to receive notifications of new posts by email.

    I won't send you spam. Unsubscribe at any time.

    Buy My Thinking Analytically Book!

    Cover for my book, Thinking Analytically: An Guide for Making Data-Driven Decisions.

    Top Posts

    • F-table
    • Cronbach’s Alpha: Definition, Calculations & Example
    • Z-table
    • How To Interpret R-squared in Regression Analysis
    • Accuracy vs Precision: Differences & Examples
    • Box Plot Explained with Examples
    • Interpreting Correlation Coefficients
    • How to Interpret P-values and Coefficients in Regression Analysis
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • T-Distribution Table of Critical Values

    Recent Posts

    • Data Collection Methods: Step-By-Step Guide with Examples
    • ANOVA Calculator
    • Positive Predictive Value: Meaning, Formula, and Interpretation
    • Median Absolute Deviation Calculator
    • Median Absolute Deviation: Definition, Finding & Formula
    • Outlier Calculator

    Recent Comments

    • Skata na fas on Comparing Regression Lines with Hypothesis Tests
    • Jim Frost on Comparing Regression Lines with Hypothesis Tests
    • Skata na fas on Comparing Regression Lines with Hypothesis Tests
    • Skata na fas on Comparing Regression Lines with Hypothesis Tests
    • Jim Frost on Pareto Chart: Making, Reading & Examples

    Copyright © 2026 · Jim Frost · Privacy Policy