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Interpretation of R output from Cohen's Kappa

Strange values of Cohen's kappaCohen's Kappa using (irr) and kappa2() outputs NaNCohen's Kappa, why not simple ratioWhy is Cohen's kappa low despite high observed agreement?Cohen's kappa with three categories of variableExplain Cohen's kappa in a simplest way?Inter-rater reliability - when Cohen's Kappa doesn't workCohen's Kappa: is it valid to average kappa for different rater pairs across multiple trials?Cohen's kappa for repeated measures longitudinal dataInterpreting SPSS Cohen's Kappa output

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;

3

2

$begingroup$

I have the following result from carrying out Cohen's kappa in R

library(irr)
n = 100
o = c(rep(0,n), rep(1,n))
p = c(rbinom(n,1,0.5), rbinom(n,1,0.51))
k = kappa2(
 data.frame(p,o), "unweighted"
)
k

Which outputs

 Cohen's Kappa for 2 Raters (Weights: unweighted)

 Subjects = 200 
 Raters = 2 
 Kappa = -0.08 

 z = -1.13 
 p-value = 0.258 

My interpretation of this

the test is displaying that there seems to be disagreement between the two vectors as kappa is negative. However, given the p value of 0.258 we can't say that this disagreement is significant, and may just be down to chance.

If
someone could highlight if there is anything I'm missing from this interpretation that would be appreciated.

hypothesis-testing model-comparison agreement-statistics association-measure cohens-kappa

share|cite|improve this question

edited Apr 19 at 17:32

baxx

asked Apr 19 at 14:08

baxxbaxx

320111

$endgroup$

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  $begingroup$
  Please use seeded-random data (set.seed()) so we get a reproducible example. Also, try other package implementations such as DescTools::CohenKappa(), it gives you lower and upper confidence intervals which might be more meaningful to decide whether you can conclude there was no agreement/disagreement.
  $endgroup$
  – smci
  Apr 23 at 8:45
  
  

  
  
  
  

add a comment | 

3

2

$begingroup$

I have the following result from carrying out Cohen's kappa in R

library(irr)
n = 100
o = c(rep(0,n), rep(1,n))
p = c(rbinom(n,1,0.5), rbinom(n,1,0.51))
k = kappa2(
 data.frame(p,o), "unweighted"
)
k

Which outputs

 Cohen's Kappa for 2 Raters (Weights: unweighted)

 Subjects = 200 
 Raters = 2 
 Kappa = -0.08 

 z = -1.13 
 p-value = 0.258 

My interpretation of this

the test is displaying that there seems to be disagreement between the two vectors as kappa is negative. However, given the p value of 0.258 we can't say that this disagreement is significant, and may just be down to chance.

If
someone could highlight if there is anything I'm missing from this interpretation that would be appreciated.

hypothesis-testing model-comparison agreement-statistics association-measure cohens-kappa

share|cite|improve this question

edited Apr 19 at 17:32

baxx

asked Apr 19 at 14:08

baxxbaxx

320111

$endgroup$

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  $begingroup$
  Please use seeded-random data (set.seed()) so we get a reproducible example. Also, try other package implementations such as DescTools::CohenKappa(), it gives you lower and upper confidence intervals which might be more meaningful to decide whether you can conclude there was no agreement/disagreement.
  $endgroup$
  – smci
  Apr 23 at 8:45
  
  

  
  
  
  

add a comment | 

$begingroup$

I have the following result from carrying out Cohen's kappa in R

library(irr)
n = 100
o = c(rep(0,n), rep(1,n))
p = c(rbinom(n,1,0.5), rbinom(n,1,0.51))
k = kappa2(
 data.frame(p,o), "unweighted"
)
k

Which outputs

 Cohen's Kappa for 2 Raters (Weights: unweighted)

 Subjects = 200 
 Raters = 2 
 Kappa = -0.08 

 z = -1.13 
 p-value = 0.258 

My interpretation of this

the test is displaying that there seems to be disagreement between the two vectors as kappa is negative. However, given the p value of 0.258 we can't say that this disagreement is significant, and may just be down to chance.

If
someone could highlight if there is anything I'm missing from this interpretation that would be appreciated.

hypothesis-testing model-comparison agreement-statistics association-measure cohens-kappa

share|cite|improve this question

edited Apr 19 at 17:32

baxx

asked Apr 19 at 14:08

baxxbaxx

320111

$endgroup$

library(irr)
n = 100
o = c(rep(0,n), rep(1,n))
p = c(rbinom(n,1,0.5), rbinom(n,1,0.51))
k = kappa2(
 data.frame(p,o), "unweighted"
)
k

 Cohen's Kappa for 2 Raters (Weights: unweighted)

 Subjects = 200 
 Raters = 2 
 Kappa = -0.08 

 z = -1.13 
 p-value = 0.258 

share|cite|improve this question

edited Apr 19 at 17:32

baxx

asked Apr 19 at 14:08

baxxbaxx

320111

share|cite|improve this question

edited Apr 19 at 17:32

baxx

asked Apr 19 at 14:08

baxxbaxx

320111

asked Apr 19 at 14:08

baxxbaxx

320111

asked Apr 19 at 14:08

baxxbaxx

320111

1 Answer
1

active

oldest

votes

3

$begingroup$

From the perspective of an applied analyst:

First note: that disagreement means if rater A says 1 rater B says 0; it is like how a Pearson correlation of -1 denotes a strong, albeit negative, relationship. The actual null hypothesis here is: what rater A says has no relation to what rater B says.

I wouldn't make such vague yet absolute declarations such as "there seems to be disagreement" (or rather there seems to be no agreement). It is not really an appropriate summary of data without significant background and context. If we had that background and context (such as in a discussions section), we could contribute some nuanced synthesis of the result, pointing to improvements or reasons for disagreement, etc.

To interpret the results:

• report the percentage agreement, note if any one category was more prevalent (a case when % agreement may be high but $kappa$ may be low)


• state the kappa statistic and it's confidence interval


• I often question the worth of a p-value where the null hypothesis is a stupid case of "no agreement", but you can quote the p-value and say that the data did not provide evidence that the raters agree.

share|cite|improve this answer

edited Apr 23 at 11:00

smci

89911018

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143

$endgroup$

add a comment | 

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3

$begingroup$

From the perspective of an applied analyst:

First note: that disagreement means if rater A says 1 rater B says 0; it is like how a Pearson correlation of -1 denotes a strong, albeit negative, relationship. The actual null hypothesis here is: what rater A says has no relation to what rater B says.

I wouldn't make such vague yet absolute declarations such as "there seems to be disagreement" (or rather there seems to be no agreement). It is not really an appropriate summary of data without significant background and context. If we had that background and context (such as in a discussions section), we could contribute some nuanced synthesis of the result, pointing to improvements or reasons for disagreement, etc.

To interpret the results:

• report the percentage agreement, note if any one category was more prevalent (a case when % agreement may be high but $kappa$ may be low)


• state the kappa statistic and it's confidence interval


• I often question the worth of a p-value where the null hypothesis is a stupid case of "no agreement", but you can quote the p-value and say that the data did not provide evidence that the raters agree.

share|cite|improve this answer

edited Apr 23 at 11:00

smci

89911018

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143

$endgroup$

add a comment | 

3

$begingroup$

From the perspective of an applied analyst:

First note: that disagreement means if rater A says 1 rater B says 0; it is like how a Pearson correlation of -1 denotes a strong, albeit negative, relationship. The actual null hypothesis here is: what rater A says has no relation to what rater B says.

I wouldn't make such vague yet absolute declarations such as "there seems to be disagreement" (or rather there seems to be no agreement). It is not really an appropriate summary of data without significant background and context. If we had that background and context (such as in a discussions section), we could contribute some nuanced synthesis of the result, pointing to improvements or reasons for disagreement, etc.

To interpret the results:

• report the percentage agreement, note if any one category was more prevalent (a case when % agreement may be high but $kappa$ may be low)


• state the kappa statistic and it's confidence interval


• I often question the worth of a p-value where the null hypothesis is a stupid case of "no agreement", but you can quote the p-value and say that the data did not provide evidence that the raters agree.

share|cite|improve this answer

edited Apr 23 at 11:00

smci

89911018

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143

$endgroup$

add a comment | 

$begingroup$

From the perspective of an applied analyst:

First note: that disagreement means if rater A says 1 rater B says 0; it is like how a Pearson correlation of -1 denotes a strong, albeit negative, relationship. The actual null hypothesis here is: what rater A says has no relation to what rater B says.

I wouldn't make such vague yet absolute declarations such as "there seems to be disagreement" (or rather there seems to be no agreement). It is not really an appropriate summary of data without significant background and context. If we had that background and context (such as in a discussions section), we could contribute some nuanced synthesis of the result, pointing to improvements or reasons for disagreement, etc.

To interpret the results:

• report the percentage agreement, note if any one category was more prevalent (a case when % agreement may be high but $kappa$ may be low)


• state the kappa statistic and it's confidence interval


• I often question the worth of a p-value where the null hypothesis is a stupid case of "no agreement", but you can quote the p-value and say that the data did not provide evidence that the raters agree.

share|cite|improve this answer

edited Apr 23 at 11:00

smci

89911018

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143

$endgroup$

share|cite|improve this answer

edited Apr 23 at 11:00

smci

89911018

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143

edited Apr 23 at 11:00

smci

89911018

edited Apr 23 at 11:00

smci

89911018

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143

answered Apr 19 at 14:30

AdamOAdamO

35.3k266143