Fleiss Kappa Calculator

A precise online calculator for Fleiss' kappa, designed for researchers and analysts to measure agreement among multiple raters across several categories.

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About This Tool

Fleiss' kappa is a summary statistic for the level of agreement among a fixed number of raters when assigning categorical ratings to a set of items. This tool computes the classic unweighted Fleiss' kappa from an n x k rating matrix, where n is the number of subjects and k is the number of categories. The calculation relies on the per-subject category counts n_ij and the total number of raters per subject. It produces P_bar, the mean agreement, and P_bar_e, the chance agreement based on category frequencies, then yields kappa = (P_bar - P_bar_e) / (1 - P_bar_e). The tool validates that each row sums to the same number of raters and that all counts are non-negative integers.

How to Use

  1. Provide inputs: specify the number of subjects, the number of categories, and the ratings matrix as rows.
  2. Validate data: ensure each row sums to the same total (the number of raters) and all entries are non-negative integers.
  3. Run calculation: the tool computes P_bar, P_bar_e, kappa, and optional statistics.
  4. Review outputs: examine the kappa value, standard error, and confidence interval to assess agreement.
  5. Export or reference: copy results or export the numeric summary for reporting.
How to use fleiss kappa online calculator

Frequently Asked Questions

Find Quick Answers

What is Fleiss' kappa?
Fleiss' kappa measures the degree of agreement among multiple raters across several categories, adjusting for chance agreement. It extends Cohen's kappa to more than two raters.
What data format is required?
Provide a matrix where each row corresponds to a subject, columns to categories, and cell values are non-negative integers indicating how many raters chose that category. Each row must sum to the same total number of raters.
How should I interpret the score?
Kappa ranges from -1 to 1. Rough interpretation: <0 no agreement, 0–0.20 slight, 0.21–0.40 fair, 0.41–0.60 moderate, 0.61–0.80 substantial, 0.81–1.00 almost perfect. Confidence intervals provide precision.
What if data is inconsistent?
If rows have differing totals or negative counts, the tool flags errors and requires correction. Inconsistent data prevents valid kappa computation until normalized.

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