Cohen's kappa coefficient (κ) is a statistic which measures interrater agreement for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation, as κ takes into account the possibility of the agreement occurring by chance. There is controversy surrounding Cohen's kappa due to the difficulty in interpreting indices of agreement. Some researchers have suggested that it is conceptually simpler to evaluate disagreement between items.^{[1]} See the Limitations section for more detail.
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Transcription
Contents
Calculation
Cohen's kappa measures the agreement between two raters who each classify N items into C mutually exclusive categories. The first mention of a kappalike statistic is attributed to Galton (1892);^{[2]} see Smeeton (1985).^{[3]}
The definition of is:
where p_{o} is the relative observed agreement among raters (identical to accuracy), and p_{e} is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly seeing each category. If the raters are in complete agreement then . If there is no agreement among the raters other than what would be expected by chance (as given by p_{e}), . It is possible for the statistic to be negative,^{[4]} which implies that there is no effective agreement between the two raters or the agreement is worse than random.
For categories k, number of items N and the number of times rater i predicted category k:
The seminal paper introducing kappa as a new technique was published by Jacob Cohen in the journal Educational and Psychological Measurement in 1960.^{[5]}
A similar statistic, called pi, was proposed by Scott (1955). Cohen's kappa and Scott's pi differ in terms of how p_{e} is calculated.
Note that Cohen's kappa measures agreement between two raters only. For a similar measure of agreement (Fleiss' kappa) used when there are more than two raters, see Fleiss (1971). The Fleiss kappa, however, is a multirater generalization of Scott's pi statistic, not Cohen's kappa. Kappa is also used to compare performance in machine learning, but the directional version known as Informedness or Youden's J statistic is argued to be more appropriate for supervised learning.^{[6]}
Example
Suppose that you were analyzing data related to a group of 50 people applying for a grant. Each grant proposal was read by two readers and each reader either said "Yes" or "No" to the proposal. Suppose the disagreement count data were as follows, where A and B are readers, data on the main diagonal of the matrix (a and d) count the number of agreements and offdiagonal data (b and c) count the number of disagreements:
B  

Yes  No  
A  Yes  a  b 
No  c  d 
e.g.
B  

Yes  No  
A  Yes  20  5 
No  10  15 
The observed proportionate agreement is:
To calculate p_{e} (the probability of random agreement) we note that:
 Reader A said "Yes" to 25 applicants and "No" to 25 applicants. Thus reader A said "Yes" 50% of the time.
 Reader B said "Yes" to 30 applicants and "No" to 20 applicants. Thus reader B said "Yes" 60% of the time.
So the expected probability that both would say yes at random is:
Similarly:
Overall random agreement probability is the probability that they agreed on either Yes or No, i.e.:
So now applying our formula for Cohen's Kappa we get:
Same percentages but different numbers
A case sometimes considered to be a problem with Cohen's Kappa occurs when comparing the Kappa calculated for two pairs of raters with the two raters in each pair having the same percentage agreement but one pair give a similar number of ratings in each class while the other pair give a very different number of ratings in each class.^{[7]} (In the cases below, notice B has 70 yeses and 30 nos, in the first case, but those numbers are reversed in the second.) For instance, in the following two cases there is equal agreement between A and B (60 out of 100 in both cases) in terms of agreement in each class, so we would expect the relative values of Cohen's Kappa to reflect this. However, calculating Cohen's Kappa for each:
B  

Yes  No  
A  Yes  45  15 
No  25  15 
B  

Yes  No  
A  Yes  25  35 
No  5  35 
we find that it shows greater similarity between A and B in the second case, compared to the first. This is because while the percentage agreement is the same, the percentage agreement that would occur 'by chance' is significantly higher in the first case (0.54 compared to 0.46).
Significance and magnitude
Statistical significance for kappa is rarely reported, probably because even relatively low values of kappa can nonetheless be significantly different from zero but not of sufficient magnitude to satisfy investigators.^{[8]}^{:66} Still, its standard error has been described^{[9]} and is computed by various computer programs.^{[10]}
If statistical significance is not a useful guide, what magnitude of kappa reflects adequate agreement? Guidelines would be helpful, but factors other than agreement can influence its magnitude, which makes interpretation of a given magnitude problematic. As Sim and Wright noted, two important factors are prevalence (are the codes equiprobable or do their probabilities vary) and bias (are the marginal probabilities for the two observers similar or different). Other things being equal, kappas are higher when codes are equiprobable. On the other hand, Kappas are higher when codes are distributed asymmetrically by the two observers. In contrast to probability variations, the effect of bias is greater when Kappa is small than when it is large.^{[11]}^{:261–262}
Another factor is the number of codes. As number of codes increases, kappas become higher. Based on a simulation study, Bakeman and colleagues concluded that for fallible observers, values for kappa were lower when codes were fewer. And, in agreement with Sim & Wrights's statement concerning prevalence, kappas were higher when codes were roughly equiprobable. Thus Bakeman et al. concluded that "no one value of kappa can be regarded as universally acceptable."^{[12]}^{:357} They also provide a computer program that lets users compute values for kappa specifying number of codes, their probability, and observer accuracy. For example, given equiprobable codes and observers who are 85% accurate, value of kappa are 0.49, 0.60, 0.66, and 0.69 when number of codes is 2, 3, 5, and 10, respectively.
Nonetheless, magnitude guidelines have appeared in the literature. Perhaps the first was Landis and Koch,^{[13]} who characterized values < 0 as indicating no agreement and 0–0.20 as slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1 as almost perfect agreement. This set of guidelines is however by no means universally accepted; Landis and Koch supplied no evidence to support it, basing it instead on personal opinion. It has been noted that these guidelines may be more harmful than helpful.^{[14]} Fleiss's^{[15]}^{:218} equally arbitrary guidelines characterize kappas over 0.75 as excellent, 0.40 to 0.75 as fair to good, and below 0.40 as poor.
Weighted kappa
The weighted kappa allows disagreements to be weighted differently^{[16]} and is especially useful when codes are ordered.^{[8]}^{:66} Three matrices are involved, the matrix of observed scores, the matrix of expected scores based on chance agreement, and the weight matrix. Weight matrix cells located on the diagonal (upperleft to bottomright) represent agreement and thus contain zeros. Offdiagonal cells contain weights indicating the seriousness of that disagreement. Often, cells one off the diagonal are weighted 1, those two off 2, etc.
The equation for weighted κ is:
where k=number of codes and , , and are elements in the weight, observed, and expected matrices, respectively. When diagonal cells contain weights of 0 and all offdiagonal cells weights of 1, this formula produces the same value of kappa as the calculation given above.
Kappa maximum
Kappa assumes its theoretical maximum value of 1 only when both observers distribute codes the same, that is, when corresponding row and column sums are identical. Anything less is less than perfect agreement. Still, the maximum value kappa could achieve given unequal distributions helps interpret the value of kappa actually obtained. The equation for κ maximum is:^{[17]}
where , as usual, ,
k = number of codes, are the row probabilities, and are the column probabilities.
Limitations
Kappa is an index that considers observed agreement with respect to a baseline agreement. However, investigators must consider carefully whether Kappa's baseline agreement is relevant for the particular research question. Kappa's baseline is frequently described as the agreement due to chance, which is only partially correct. Kappa's baseline agreement is the agreement that would be expected due to random allocation, given the quantities specified by the marginal totals of square contingency table. Thus, Kappa = 0 when the observed allocation is apparently random, regardless of the quantity disagreement as constrained by the marginal totals. However, for many applications, investigators should be more interested in the quantity disagreement in the marginal totals than in the allocation disagreement as described by the additional information on the diagonal of the square contingency table. Thus for many applications, Kappa's baseline is more distracting than enlightening. Consider the following example:
Reference  

G  R  
Comparison  G  1  14 
R  0  1 
The disagreement proportion is 14/16 or .875. The disagreement is due to quantity because allocation is optimal. Kappa is .01.
Reference  

G  R  
Comparison  G  0  1 
R  1  14 
The disagreement proportion is 2/16 or .125. The disagreement is due to allocation because quantities are identical. Kappa is 0.07.
Here, reporting quantity and allocation disagreement is informative while Kappa obscures information. Furthermore, Kappa introduces some challenges in calculation and interpretation because Kappa is a ratio. It is possible for Kappa's ratio to return an undefined value due to zero in the denominator. Furthermore, a ratio does not reveal its numerator nor its denominator. It is more informative for researchers to report disagreement in two components, quantity and allocation. These two components describe the relationship between the categories more clearly than a single summary statistic. When predictive accuracy is the goal, researchers can more easily begin to think about ways to improve a prediction by using two components of quantity and allocation, rather than one ratio of Kappa.^{[1]}
Some researchers have expressed concern over κ's tendency to take the observed categories' frequencies as givens, which can make it unreliable for measuring agreement in situations such as the diagnosis of rare diseases. In these situations, κ tends to underestimate the agreement on the rare category.^{[18]} For this reason, κ is considered an overly conservative measure of agreement.^{[19]} Others^{[20]}^{[citation needed]} contest the assertion that kappa "takes into account" chance agreement. To do this effectively would require an explicit model of how chance affects rater decisions. The socalled chance adjustment of kappa statistics supposes that, when not completely certain, raters simply guess—a very unrealistic scenario.
See also
References
 ^ ^{a} ^{b} Pontius, Robert; Millones, Marco (2011). "Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment". International Journal of Remote Sensing. 32 (15): 4407–4429. doi:10.1080/01431161.2011.552923.
 ^ Galton, F. (1892). Finger Prints Macmillan, London.
 ^ Smeeton, N.C. (1985). "Early History of the Kappa Statistic". Biometrics. 41 (3): 795. JSTOR 2531300.
 ^ "The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements". Physical Therapy. 2005. doi:10.1093/ptj/85.3.257. ISSN 15386724.
 ^ Cohen, Jacob (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement. 20 (1): 37–46. doi:10.1177/001316446002000104.
 ^ Powers, David M. W. (2012). "The Problem with Kappa" (PDF). Conference of the European Chapter of the Association for Computational Linguistics (EACL2012) Joint ROBUSUNSUP Workshop. Archived from the original (PDF) on 20160518.
 ^ Kilem Gwet (May 2002). "InterRater Reliability: Dependency on Trait Prevalence and Marginal Homogeneity" (PDF). Statistical Methods for InterRater Reliability Assessment. 2: 1–10.
 ^ ^{a} ^{b} Bakeman, R.; Gottman, J.M. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). Cambridge, UK: Cambridge University Press. ISBN 9780521275934.
 ^ Fleiss, J.L.; Cohen, J.; Everitt, B.S. (1969). "Large sample standard errors of kappa and weighted kappa". Psychological Bulletin. 72 (5): 323–327. doi:10.1037/h0028106.
 ^ Robinson, B.F; Bakeman, R. (1998). "ComKappa: A Windows 95 program for calculating kappa and related statistics". Behavior Research Methods, Instruments, and Computers. 30 (4): 731–732. doi:10.3758/BF03209495.
 ^ Sim, J; Wright, C. C (2005). "The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements". Physical Therapy. 85 (3): 257–268. PMID 15733050.
 ^ Bakeman, R.; Quera, V.; McArthur, D.; Robinson, B. F. (1997). "Detecting sequential patterns and determining their reliability with fallible observers". Psychological Methods. 2 (4): 357–370. doi:10.1037/1082989X.2.4.357.
 ^ Landis, J.R.; Koch, G.G. (1977). "The measurement of observer agreement for categorical data". Biometrics. 33 (1): 159–174. doi:10.2307/2529310. JSTOR 2529310. PMID 843571.
 ^ Gwet, K. (2010). "Handbook of InterRater Reliability (Second Edition)" ISBN 9780970806222^{[page needed]}
 ^ Fleiss, J.L. (1981). Statistical methods for rates and proportions (2nd ed.). New York: John Wiley. ISBN 9780471263708.
 ^ Cohen, J. (1968). "Weighed kappa: Nominal scale agreement with provision for scaled disagreement or partial credit". Psychological Bulletin. 70 (4): 213–220. doi:10.1037/h0026256. PMID 19673146.
 ^ Umesh, U. N.; Peterson, R.A.; Sauber M. H. (1989). "Interjudge agreement and the maximum value of kappa". Educational and Psychological Measurement. 49 (4): 835–850. doi:10.1177/001316448904900407.
 ^ Viera, Anthony J.; Garrett, Joanne M. (2005). "Understanding interobserver agreement: the kappa statistic". Family Medicine. 37 (5): 360–363.
 ^ Strijbos, J.; Martens, R.; Prins, F.; Jochems, W. (2006). "Content analysis: What are they talking about?". Computers & Education. 46: 29–48. CiteSeerX 10.1.1.397.5780. doi:10.1016/j.compedu.2005.04.002.
 ^ Uebersax, JS. (1987). "Diversity of decisionmaking models and the measurement of interrater agreement" (PDF). Psychological Bulletin. 101: 140–146. CiteSeerX 10.1.1.498.4965. doi:10.1037/00332909.101.1.140.
Further reading
 Banerjee, M.; Capozzoli, Michelle; McSweeney, Laura; Sinha, Debajyoti (1999). "Beyond Kappa: A Review of Interrater Agreement Measures". The Canadian Journal of Statistics. 27 (1): 3–23. doi:10.2307/3315487. JSTOR 3315487.
 Brennan, R. L.; Prediger, D. J. (1981). "Coefficient λ: Some Uses, Misuses, and Alternatives". Educational and Psychological Measurement. 41 (3): 687–699. doi:10.1177/001316448104100307.
 Cohen, Jacob (1960). "A coefficient of agreement for nominal scales". Educational and Psychological Measurement. 20 (1): 37–46. doi:10.1177/001316446002000104.
 Cohen, J. (1968). "Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit". Psychological Bulletin. 70 (4): 213–220. doi:10.1037/h0026256. PMID 19673146.
 Fleiss, J.L. (1971). "Measuring nominal scale agreement among many raters". Psychological Bulletin. 76 (5): 378–382. doi:10.1037/h0031619.
 Fleiss, J. L. (1981) Statistical methods for rates and proportions. 2nd ed. (New York: John Wiley) pp. 38–46
 Fleiss, J.L.; Cohen, J. (1973). "The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability". Educational and Psychological Measurement. 33 (3): 613–619. doi:10.1177/001316447303300309.
 Gwet, Kilem L. (2014) Handbook of InterRater Reliability, Fourth Edition, (Gaithersburg : Advanced Analytics, LLC) ISBN 9780970806284
 Gwet, K. (2008). "Computing interrater reliability and its variance in the presence of high agreement" (PDF). British Journal of Mathematical and Statistical Psychology. 61 (Pt 1): 29–48. doi:10.1348/000711006X126600. PMID 18482474.
 Gwet, K. (2008). "Variance Estimation of NominalScale InterRater Reliability with Random Selection of Raters" (PDF). Psychometrika. 73 (3): 407–430. doi:10.1007/s1133600790548.
 Gwet, K. (2008). "Intrarater Reliability." Wiley Encyclopedia of Clinical Trials, Copyright 2008 John Wiley & Sons, Inc.
 Scott, W. (1955). "Reliability of content analysis: The case of nominal scale coding". Public Opinion Quarterly. 17 (3): 321–325. doi:10.1086/266577.
 Sim, J.; Wright, C. C. (2005). "The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements". Physical Therapy. 85 (3): 257–268. PMID 15733050.
External links
 Kappa, its meaning, problems, and several alternatives
 Kappa Statistics: Pros and Cons
 Windows program for kappa, weighted kappa, and kappa maximum
 Java and PHP implementation of weighted Kappa
Online calculators
 Cohen's Kappa for Maps
 Online (Multirater) Kappa Calculator
 Online Kappa Calculator (multiple raters and variables)
 Cohen's Kappa