Evaluating Consistency of Ratings: Understanding and Applying Cohen’s Kappa Metric

May 21, 2024 Comment:0 Business Intelligence


Many activities in our day to day work involve some kind of evaluation or judgment. When this is based on quantitative measurement and criteria, we will not be surprised if different evaluators give the same rating.

But many evaluations, even if based on-well defined and well understood criteria, are subjective and at the discretion of the evaluator. In such cases, how can we have confidence that different people give similar ratings? How do we know that the rating is not dependent on the luck of the draw, based on who did the evaluation?

Cohen’s Kappa

The Cohen’s Kappa metric gives an objective value to the reliability of evaluation by different raters rating the same item.  If two raters agree on the rating, we will feel more confident that the rating is correct.  This has applications in many areas, such as two doctors diagnosing a disease from the same medical reports, two teachers grading the same student essays, or two food critics pronouncing on the same piece of cake.

Let there be two recruiters R1 and R2, deciding on whether or not to shortlist resumes for a particular open job position.  If each recruiter evaluates the same 100 resumes independent of the other, we can use the data from their decision to find how consistent the decision is, using Cohen’s Kappa metric.  Suppose we have the following result.

consistent the decision is, using Cohen’s Kappa metric

Figure 1: Same 100 Resumes Evaluated by Two Different Raters

We can see that both raters agreed on shortlisting 30 resumes and also agreed on rejecting 56 resumes.  So they agreed with each other in 86 out of 100 cases, for an agreement ratio of 0.86.  This is the “accuracy” metric, one that is intuitive and easy to understand.  This would make it seem that our shortlisting process was very reliable.  But can we really so conclude from the data?

Suppose one or both the raters, instead of their knowledge and experience, used a purely random process, like rolling a dice or tossing a coin, to decide whether to shortlist or reject a resume.  Even here, we could expect to find them both agreeing in at least some of the cases, purely by chance.

Calculating Cohen’s Kappa

The Cohen’s Kappa metric removes the element of chance agreement between the raters so as to give a better measure of the consistency of their ratings.  Here is how it is defined and calculated.


We find N1, the ratio of observations where the raters agree, to the total number of observations.  In our example, we have

N1 = 30 + 56) / (100)

This works out to 0.86, as we have already seen above.


We now have to find the ratio of observations where the agreement can be attributed to chance.  For this, we find N2, the ratio of observations where both raters shortlisted a resume based on random factors.  It is found by calculating the probability of R1 shortlisting a resume, multiplied by the probability of R2 shortlisting a resume.  So

N2 = ((30 + 9) / (100)) * ((30 + 5) / (100))

This is because R1 shortlisted (30 + 9) resumes out of 100, while R2 shortlisted (30 + 5) out of 100. Thus the probability of R1 shortlisting a resume is 0.39, while that of R2 shortlisting a resume is 0.35.

Therefore the probability of both raters shortlisting a resume by pure chance is (0.39 * 0.35) = 0.1365.


We next find the ratio of observations N3 where both raters rejected a resume based on chance.  Using the same method as in Step-2 above,

N3 = ((56 + 5) / (100)) * ((56 + 9) / (100))

The value of N3 is thus (0.61 * 0.65) = 0.3965.  This is the probability that both raters reject a resume by pure chance.


From N2 and N3, we can find N4, the probability that the raters will agree on a resume by pure chance.

N4 = N2 + N3

N4 = 0.1365 + 0.3965

N4 = 0.533


Now it only remains to find the value of Cohen’s Kappa.  It is defined as

K = (N1 – N4) / (1 – N4)

K = (0.86 – 0.533) / (1 – 0.533)

K = (0.327 / 0.467)

K = 0.70

This is our measure of reliability of the resume evaluation by the raters R1 and R2.

Interpreting Cohen’s Kappa

The question that now arises is whether this Kappa value is good, bad, or somewhere in between.  While there is no hard and fast rule for this, Cohen himself suggested the following
Interpreting Cohen’s Kappa
The Cohen’s Kappa metric can take values from -1 to +1.  A value of -1 indicates perfect disagreement between the raters, and any value below 0 indicates that they disagree.  However, in practice, it is quite unlikely that we find actual occurrences of this.  A value of -1 can happen only if we have a table like the following.
Hypothetical Table with Cohen’s Kappa of -1

Figure 2: Hypothetical Table with Cohen’s Kappa of -1


The Cohen’s Kappa metric is useful when we have two raters evaluating the same item independently in the same fashion.  The result categories must not overlap, that is, an item cannot fall into more than one category.  The raters must be fixed and cannot be changing from one item evaluation to another.  For such situations and other scenarios, different metrics are called for.

This metric removes the element of chance agreement between the raters and gives a more reliable number to measure consistency between them.

The metric does not and cannot evaluate the validity of the criteria used!  In our example, if the raters started using the physical appearance of the candidates as a criterion for shortlisting their resume, we may still get a good Cohen’s Kappa measure, but it would still be invalid and worthless as far as the quality of the actual shortlist goes.  So the metric only tells us how consistent the raters are, and nothing more.

This metric can be used to evaluate a software system.  If we used some resume shortlisting software, and called it R1, and also had a human rater evaluate the same resumes as R2, the Cohen’s Kappa could tell us how good the software was in shortlisting the resumes.  We could then take a decision on whether to shortlist all resumes automatically using the software, and dispense with human evaluation.


Metrics are a fascinating field of study and fun to explore.  Whenever we use a metric, we need to be very clear about what it measures, when it is applicable and is the correct metric to use, and how to interpret the number it calculates for us.  The wrong metric can lead us to wrong or even disastrous conclusions, while the correct metric enables us to understand the situation and to plan and take decisions without getting swayed by our feelings and emotions.  In a business setting and context, that is most often the correct course of action.