Matthijs J Warrens*
Warrens Institute of Psychology, Unit Methodology and Statistics, Leiden University, Netherlands
Received date:: April 23, 2015; Accepted date:: July 21, 2015; Published date:: July 28, 2015
Citation: Warrens MJ (2015) Five Ways to Look at Cohen’s Kappa. J Psychol Psychother 5:197.doi:10.4172/2161-0487.1000197
Copyright: © 2015 Warrens MJ. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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The kappa statistic is commonly used for quantifying inter-rater agreement on a nominal scale. In this review article we discuss five interpretations of this popular coefficient. Kappa is a function of the proportion of observed and expected agreement, and it may be interpreted as the proportion of agreement corrected for chance. Furthermore, kappa may be interpreted as the average category reliability as well as an intraclass correlation.
Inter-rater reliability; Inter-rater agreement; Category reliability; Average category reliability
An important form of measurement in behavioral, social and medical sciences is nominal classification, that is, the assignment of subjects to qualitative categories, as in psychiatric diagnosis. If the rater (clinician, psychologist) did not fully understand what he or she was asked to interpret, or if the definition of the categories is ambiguous, the reliability of the ratings may be poor. A poor diagnosis will limit the possible degree of association between diagnosis and anything else.
To assess the reliability of a rating instrument researchers typically ask two raters to classify the same group of subjects independently. The pairwise ratings of a group of subjects into nominal categories are often summarized in a contingency table. Because the row labels and column labels of this contingency table are identical, the table is usually called an agreement table. Table 1 is an example of an agreement table. It contains the relative frequencies of the pairwise ratings of 100 patients by two clinicians into four categories: 1 = Schizophrenia, 2 = Bipolar disorder, 3 = Depression and 4 = other.
Table 2 and 3 are two other examples of agreement tables. Formal assessments in education nowadays consist of both numerical and contextual mathematics problems. An international reform has introduced various new solution strategies of multi digit mathematics problems.
|1 = Schizophrenia||0.23||0.01||0.01||0.00||0.25|
|2 = Bipolar disorder||0.00||0.20||0.01||0.02||0.23|
|3 = Depression||0.01||0.02||0.21||0.04||0.28|
|4 = Other||0.01||0.02||0.04||0.17||0.24|
Table 1: Relative frequencies of hypothetical diagnoses of 100 patients by two clinicians.
|1 = Long division||0.20||0.00||0.03||0.23|
|2 = Repeated subtraction||0.05||0.30||0.00||0.35|
|3 = Repeated addition||0.00||0.02||0.40||0.42|
Table 2: Relative frequencies of hypothetical solution strategies of 100 students coded by two psychologists.
|1 = White matter||0.45||0.02||0.00||0.47|
|2 = Grey matter||0.02||0.45||0.00||0.47|
|3 = Cerebral spinal fluid||0.00||0.01||0.05||0.06|
Table 3: Relative frequencies of hypothetical classifications of 8000 voxels by two algorithms.
The traditional algorithm in complex division is long division, whereas modern solution strategies are repeated subtraction and repeated addition. Table 2 contains the relative frequencies of the pairwise codings by two educational psychologists of written solution strategies of 100 sixth graders into three categories: 1 = Long division, 2 = Repeated subtraction and 3 = Repeated addition.
Magnetic Resonance Imaging (MRI) is an imaging technique that provides researchers with tools to observe noninvasively neural activity in the human brain. In MRI the brain is divided into a set of cubes, called voxels, and to interpret an image each voxel must be identified. Classification of brain tissues is usually done with software algorithms. Table 3 contains the relative frequencies of the pairwise classifications of 8000 voxels by two algorithms into three categories: 1 = White matter, 2 = Grey matter and 3 = Cerebral spinal fluid.
The agreement between the raters (or algorithms) can be used as an indicator of the quality of the categories of the rating instrument and the raters’ ability to apply them. High agreement between the ratings indicates consensus in the diagnosis and interchangeability of the ratings. Cohen’s kappa is the most commonly used statistic for assessing nominal agreement between two raters [1-7]. Kappa has value 1 if there is perfect agreement between the raters, and value 0 if the observed agreement is equal to agreement expected by chance. Several authors have suggested interpretation or benchmark guidelines for values between 0 and 1. The most commonly used guidelines are due to Landis and Koch : 0.00 - 0.20 indicates slight agreement, 0.21- 0.40 fair agreements, 0.41-0.60 moderate agreement, 0.61-0.80 substantial agreement, and 0.81-1.00 indicates almost perfect agreement. However, it should be noted that these guidelines are generally considered arbitrary.
Since its introduction kappa has been applied in thousands of research applications. Several authors have identified difficulties with the interpretation of kappa [4,9,10]. For example, kappa depends on the base rates of the categories. The base rates reflect how often the categories were used by the raters. Kappas from samples with different base rates are therefore not comparable [6,9,10]. However, since kappa has several useful interpretations, it is likely that it continues to be a standard tool for summarizing inter-rater agreement on a nominal scale in the near future [1,3,4,11,12].
The kappa statistic was introduced by Cohen  in 1960. However, the basic idea of an agreement measure was anticipated substantially before 1960. For example, decades earlier Corrado Gini already considered measures for assessing agreement on a nominal scale [11,13,14]. Furthermore, Cohen’s paper  was a response to Bennett et al.  and Scott  a few years before. The measure in Scott  is closely related to kappa, and is also-known as the intraclass kappa . Although it was not the first measure of inter-rater agreement on a nominal scale, kappa is the most widely used agreement measure [1,3,4,11,18].
In this article we discuss five ways to look at kappa. Following Cohen and others our focus is on kappa as a computational index. We present several algebraic interpretations. Since computation of a sample kappa requires no assumptions about a population, the interpretations are distribution free. Several interpretations have been around for quite a while, while others were discovered more recently. It is not claimed here that all possible interpretations of Cohen’s kappa are discussed. For example, additional interpretations of kappa can be found in [7,17,18].
Cohen’s kappa is a dimensionless index that can be used to express the agreement between two raters in a single number. Let pii denote the proportion of patients classified into category i according to both raters. The sum of these proportions is called the proportion of observed agreement, which is given by
For the data in Table 1 we have
Furthermore, let pi+ and p+i denote the proportion of patients classified into category i by the first and second rater, respectively. The numbers pi+ and p+i are the base rates; they reflect how often the raters used category i. Using the base rates the proportion of expected agreement is given by
For the data in Table 1 we have
Next, the kappa statistic is usually defined as a function of the observed and expected agreement, given by
Equation (3) is the usual formula found in introductory statistics textbooks. For the data in Table 1 we have κ = (0.81− 0.25) / (1− 0.25) = 0.75 , which, according to the guidelines in Landis and Koch , indicates a substantial level of agreement. For the data in Table 2 we have po = 0.90, pe = 0.35 and κ = 0.85 , whereas for the data in Table 3 we have po = 0.95, pe = 0.45 and κ = 0.91. In the latter two cases agreement is almost perfect.
It is sometimes desirable that the theoretical value of an agreement measure is zero if the classifications made by the raters are statistically independent . For example, kappa has zero value under statistical independence, but the proportion of observed agreement has not. Furthermore, since raters may agree on the diagnoses simply by chance, the value of the proportion of observed agreement is generally considered to be artificially high.
If a coefficient does not have zero value under statistical independence of the raters, it can be corrected for agreement due to chance [4,19,20]. After correction for chance agreement, a measure M has a form
where expectation E(M) is the value of M under statistical independence.
The value of the observed agreement Po under statistical independence is given by E(Po) = Pe. Using the latter formula together with M = Po in equation (4) yields equation (3). Hence, kappa is the chance-corrected version of the proportion of observed agreement. We thus have the following alternative interpretation of kappa: κ = 0.75 is the proportion of agreement Po corrected for chance. Note that to calculate the value of kappa in practice one can simply use equation (3).
The number of categories used for various rating instruments varies from the minimum number of two to five in many practical applications. It is sometimes desirable to combine some of the categories, for example, when two categories are easily confused [5,7]. With m categories there are m(m-1)/2 pairs, and thus m(m-1)/2 ways to combine two categories. For example, Table 1 has four categories and there are 4(4-1)/2=6 ways to combine two categories.
Although the value of kappa may increase if two categories are combined, it is a common misconception that this is always the case [5,7]. In fact, kappa may also decrease. For example, in Table 1 there is little disagreement between the raters on the categories 1 = Schizophrenia and 2 = Bipolar. The two categories can be clearly distinguished from one another. If we combine the two categories the kappa value decreases from κ = 0.75 to κ12 = 0.17 (the subscripts 12 in κ12 denotes that categories 1 and 2 are combined). On the other hand, there is some disagreement between the raters on categories 3 = Depression and 4 = Other. If we combine these two categories kappa increase to κ34 = 0.82 , which is a substantial increase. The remaining four values of kappa that can be obtained if we combine two categories are, κ13 =κ14 = 0.72 , κ 23= 0.74 and κ24= 0.76. Thus, kappa can either increase or decrease if we combine categories, and both cases are always possible .
By combining categories the value of kappa may increase. Hence, the reliability of a rating instrument can be increased by combining appropriate categories. By combining categories one can assess the quality of the categories of the rating instrument and the raters’ ability to apply them. If two categories are combined in practice it is important to consider the (possible) substantial meaning of the new category.
The overall kappa is an average of all kappas that are obtained if we combine two categories. More precisely, the overall kappa is a weighted average of these kappas if we use the denominators of the kappas as weights. A weighted average is a mean value just like an ordinary average. For example, for Table 1 the weights are 0.63, 0.61, 0.63, 0.62, 0.64 and 0.62, respectively, and the weighted average is equal to
Thus, the overall kappa is an average of the kappas corresponding to all possible tables that can be obtained by combining two categories. We thus have the following alternative interpretation of kappa: if we combine two categories the average kappa isκ = 0.75 .
Kappa summarizes the agreement between two raters over all categories. It is frequently more informative to assess the agreement between the raters on the individual categories [7,18]. The category reliability of category i is given by
The category reliability in equation (5) reflects the agreement between the raters on category i. The statistic can also be obtained in the following way. An agreement table (for example Table 1) can be collapsed into a smaller table of size 2×2 by combining all categories other than the one of current interest (category i) into a single ``all others’’ category. The kappa value corresponding to this 2×2 table is the category reliability given in equation (5).
With m categories there are m category reliabilities, one for each category. Consider for example Table 1. The category reliability of Schizophrenia is 0.89 κi = , indicating almost perfect agreement, whereas the reliability of Bipolar is κ2 = 0.78 , indicating good agreement. The reliabilities of Depression and Other are κ3 = 0.67 and κ4 = 0.64 , respectively, indicating only moderate to good agreement. The category reliabilities illustrate that agreement is much better on the first two categories than the last two categories.
The overall kappa is an average of the individual category reliabilities. More precisely, kappa is a weighted average of the category reliabilities using the denominator of the category reliabilities as weights . For example, for Table 1 the weights are 0.19, 0.18, 0.20 and 0.18, respectively, and the weighted average is equal to
We thus have the following alternative interpretation of kappa: the average category reliability is κ = 0.75 .
The interpretation of the overall kappa as the average category reliability has the following consequence. If the category reliabilities are quite different, for example, high agreement between the raters on one category (Schizophrenia, 0.89 κi = ) but low agreement on another category (Depression, κ3 = 0.67 ), the overall kappa cannot fully reflect the complexity of the patterns of agreement between the raters. It would therefore be good practice to report both the overall kappa and the category reliabilities of an agreement table. Such practice would provide substantially more information than reporting only a single number .
While kappa is commonly used to assess agreement between two raters when subjects are classified on a nominal scale, intraclass correlations are often used when two or more raters classify the same group of subjects on a numerical scale [21-24]. An intraclass correlation describes how strongly subjects in the same group or category resemble each other.
Rae  showed that if we use the Gini-Light-Margolin concept of partitioning variance for qualitative data, then Cohen’s kappa may be interpreted as an intraclass correlation [21,23]. Let σ r2, σs 2 and σe2 denote, respectively, the rater variance, the subjects variance, and the error variance. Furthermore, let N denote the total number of subjects (= sample size). Using these definitions kappa can be written as
For large N equation (6) approximates
which is interpretable as the intraclass correlation of reliability when systematic variability among the raters is included as a component of the total variation. We thus have the following alternative interpretation of kappa: in terms of variance, the degree of resemblance of the subjects is κ = 0.75 .
In this article we reviewed five ways to look at Cohen’s kappa. Certainly there are other ways to interpret kappa [7,17,18]. We do not presume here to have summarized all useful and interesting approaches of kappa. Nevertheless, the five approaches illustrate the diversity of interpretations available to researchers who use kappa.
The various interpretations of Cohen’s kappa show the growth of this popular statistic over the past decades. Its popularity has led to the development of many extensions, including, kappas for three or more raters [20,22] and kappas for ordinal categories [23,24]. However, Cohen’s statistic in (3) is surprisingly unchanged from the one originally proposed.