Calculate interrater reliability and agreement statistics for categorical coding, content analysis, and observational studies. Supports Cohen's Kappa (2 raters), Fleiss' Kappa (3+ raters), and percent agreement. Essential for qualitative research, content analysis, and ensuring coding consistency.
Ready to measure interrater agreement? to see Cohen's Kappa in action with a sentiment analysis example, or upload your own coding data to assess reliability between your raters.
Select the columns containing each rater's codes. Each column represents one rater's assessments. Rows represent the subjects/items being coded.
Interrater reliability (also called inter-observer or inter-coder reliability) measures the degree of agreement between two or more independent raters who code, classify, or rate the same phenomenon. It's essential for establishing the objectivity and consistency of qualitative coding schemes, content analysis, and observational research.
Used for measuring agreement between two raters. Adjusts for chance agreement, providing a more conservative estimate than simple percent agreement.
Where po = observed agreement, pe = expected agreement by chance
Extends Cohen's Kappa to three or more raters. Calculates the average pairwise agreement across all rater combinations.
Where P̄ = mean observed agreement, P̄e = mean expected agreement
The simplest measure: the proportion of items on which raters agree. However, it doesn't account for chance agreement and may be misleadingly high when one category is very frequent.
Note: Different fields may use different thresholds. Some researchers use κ ≥ 0.70 as acceptable, while others require κ ≥ 0.80 for high-stakes applications.
Qualitative Content Analysis Study
Two researchers independently coded 100 social media posts into three categories: Positive, Neutral, or Negative sentiment.
Interpretation: While the raters agreed on 85% of posts, Cohen's Kappa of 0.76 indicates "substantial agreement" after accounting for chance. This is acceptable for publication and suggests the coding scheme is reliable.
Calculate Cohen's Kappa using the irr package in R:
# Install and load irr package
# install.packages("irr")
library(irr)
# Example data: Two raters coding 10 items into 3 categories
rater1 <- c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1)
rater2 <- c(1, 2, 3, 1, 3, 3, 1, 2, 2, 1)
# Combine into a data frame
ratings <- data.frame(rater1, rater2)
# Calculate Cohen's Kappa
kappa_result <- kappa2(ratings)
print(kappa_result)
# Calculate Percent Agreement
agree_result <- agree(ratings)
print(agree_result)
# For 3+ raters (Fleiss' Kappa)
rater3 <- c(1, 2, 3, 2, 2, 3, 1, 2, 3, 1)
ratings_3 <- data.frame(rater1, rater2, rater3)
fleiss_result <- kappam.fleiss(ratings_3)
print(fleiss_result)Calculate Cohen's Kappa using scikit-learn:
from sklearn.metrics import cohen_kappa_score
import numpy as np
# Example data: Two raters coding 10 items
rater1 = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1]
rater2 = [1, 2, 3, 1, 3, 3, 1, 2, 2, 1]
# Calculate Cohen's Kappa
kappa = cohen_kappa_score(rater1, rater2)
print(f"Cohen's Kappa: {kappa:.4f}")
# Calculate Percent Agreement
agreements = sum(r1 == r2 for r1, r2 in zip(rater1, rater2))
percent_agreement = (agreements / len(rater1)) * 100
print(f"Percent Agreement: {percent_agreement:.2f}%")
# Interpretation
if kappa < 0:
interpretation = "Poor"
elif kappa < 0.20:
interpretation = "Slight"
elif kappa < 0.40:
interpretation = "Fair"
elif kappa < 0.60:
interpretation = "Moderate"
elif kappa < 0.80:
interpretation = "Substantial"
else:
interpretation = "Almost Perfect"
print(f"Interpretation: {interpretation} agreement")Calculate Cohen's Kappa in SPSS:
* Calculate Cohen's Kappa for two raters.
CROSSTABS
/TABLES=rater1 BY rater2
/FORMAT=AVALUE TABLES
/STATISTICS=KAPPA
/CELLS=COUNT.
* For weighted kappa (ordinal categories).
CROSSTABS
/TABLES=rater1 BY rater2
/FORMAT=AVALUE TABLES
/STATISTICS=KAPPA(1)
/CELLS=COUNT.Calculate interrater reliability and agreement statistics for categorical coding, content analysis, and observational studies. Supports Cohen's Kappa (2 raters), Fleiss' Kappa (3+ raters), and percent agreement. Essential for qualitative research, content analysis, and ensuring coding consistency.
Ready to measure interrater agreement? to see Cohen's Kappa in action with a sentiment analysis example, or upload your own coding data to assess reliability between your raters.
Select the columns containing each rater's codes. Each column represents one rater's assessments. Rows represent the subjects/items being coded.
Interrater reliability (also called inter-observer or inter-coder reliability) measures the degree of agreement between two or more independent raters who code, classify, or rate the same phenomenon. It's essential for establishing the objectivity and consistency of qualitative coding schemes, content analysis, and observational research.
Used for measuring agreement between two raters. Adjusts for chance agreement, providing a more conservative estimate than simple percent agreement.
Where po = observed agreement, pe = expected agreement by chance
Extends Cohen's Kappa to three or more raters. Calculates the average pairwise agreement across all rater combinations.
Where P̄ = mean observed agreement, P̄e = mean expected agreement
The simplest measure: the proportion of items on which raters agree. However, it doesn't account for chance agreement and may be misleadingly high when one category is very frequent.
Note: Different fields may use different thresholds. Some researchers use κ ≥ 0.70 as acceptable, while others require κ ≥ 0.80 for high-stakes applications.
Qualitative Content Analysis Study
Two researchers independently coded 100 social media posts into three categories: Positive, Neutral, or Negative sentiment.
Interpretation: While the raters agreed on 85% of posts, Cohen's Kappa of 0.76 indicates "substantial agreement" after accounting for chance. This is acceptable for publication and suggests the coding scheme is reliable.
Calculate Cohen's Kappa using the irr package in R:
# Install and load irr package
# install.packages("irr")
library(irr)
# Example data: Two raters coding 10 items into 3 categories
rater1 <- c(1, 2, 3, 1, 2, 3, 1, 2, 3, 1)
rater2 <- c(1, 2, 3, 1, 3, 3, 1, 2, 2, 1)
# Combine into a data frame
ratings <- data.frame(rater1, rater2)
# Calculate Cohen's Kappa
kappa_result <- kappa2(ratings)
print(kappa_result)
# Calculate Percent Agreement
agree_result <- agree(ratings)
print(agree_result)
# For 3+ raters (Fleiss' Kappa)
rater3 <- c(1, 2, 3, 2, 2, 3, 1, 2, 3, 1)
ratings_3 <- data.frame(rater1, rater2, rater3)
fleiss_result <- kappam.fleiss(ratings_3)
print(fleiss_result)Calculate Cohen's Kappa using scikit-learn:
from sklearn.metrics import cohen_kappa_score
import numpy as np
# Example data: Two raters coding 10 items
rater1 = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1]
rater2 = [1, 2, 3, 1, 3, 3, 1, 2, 2, 1]
# Calculate Cohen's Kappa
kappa = cohen_kappa_score(rater1, rater2)
print(f"Cohen's Kappa: {kappa:.4f}")
# Calculate Percent Agreement
agreements = sum(r1 == r2 for r1, r2 in zip(rater1, rater2))
percent_agreement = (agreements / len(rater1)) * 100
print(f"Percent Agreement: {percent_agreement:.2f}%")
# Interpretation
if kappa < 0:
interpretation = "Poor"
elif kappa < 0.20:
interpretation = "Slight"
elif kappa < 0.40:
interpretation = "Fair"
elif kappa < 0.60:
interpretation = "Moderate"
elif kappa < 0.80:
interpretation = "Substantial"
else:
interpretation = "Almost Perfect"
print(f"Interpretation: {interpretation} agreement")Calculate Cohen's Kappa in SPSS:
* Calculate Cohen's Kappa for two raters.
CROSSTABS
/TABLES=rater1 BY rater2
/FORMAT=AVALUE TABLES
/STATISTICS=KAPPA
/CELLS=COUNT.
* For weighted kappa (ordinal categories).
CROSSTABS
/TABLES=rater1 BY rater2
/FORMAT=AVALUE TABLES
/STATISTICS=KAPPA(1)
/CELLS=COUNT.