Rather than just calculating the percentage of items that the raters agree on, Cohen’s Kappa attempts to account for the fact that the raters may happen to agree on some items purely by chance.
Cohen’s Kappa always ranges between 0 and 1, with 0 indicating no agreement between the two raters and 1 indicating perfect agreement between the two raters.
The following table summarizes how to interpret different values for Cohen’s Kappa:
The following step-by-step example shows how to calculate Cohen’s Kappa by hand.
Suppose two museum curators are asked to rate 70 paintings on whether they’re good enough to be hung in a new exhibit.
The following 2×2 table shows the results of the ratings:
Step 1: Calculate relative agreement (po) between raters.
First, we’ll calculate the relative agreement between the raters. This is simply the proportion of total ratings that the raters both said “Yes” or both said “No” on.
We can calculate this as:
Step 2: Calculate the hypothetical probability of chance agreement (pe) between raters.
Next, we’ll calculate the probability that the raters could have agreed purely by chance.
This is calculated as the total number of times that Rater 1 said “Yes” divided by the total number of responses, multiplied by the total number of times that Rater 2 said “Yes” divided by the total number of responses, added to the total number of times that Rater 1 said “No” multiplied by the total number of times that Rater 2 said “No.”
For our example, this is calculated as:
Step 3: Calculate Cohen’s Kappa
Lastly, we’ll use po and pe to calculate Cohen’s Kappa:
Cohen’s Kappa turns out to be 0.2857. Based on the table from earlier, we would say that the two raters only had a “fair” level of agreement.
You can use this Cohen’s Kappa Calculator to automatically calculate Cohen’s Kappa for any two raters.
Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.