1. Home
  2. KAPPA procedure

KAPPA procedure

Calculates a kappa coefficient of agreement for nominally scaled data (A.J. Rook).


PRINT = string token Whether to print kappa and its associated information (test); default test


DATA = tables Data sets, each consisting of an object × category table whose entries are the number of judges assigning the ith object to the jth category
STATISTIC = scalars Save the value of kappa for each data table
VARIANCE = scalars Save the corresponding variances


The kappa coefficient provides a way of assessing the agreement between judges who have rated a set of N objects or subjects using a nominal scale: that is, each judge has allocated each object to one of M different categories. The data for KAPPA, specified by the DATA parameter, consists of an N × M table whose entries indicate the number of judges that have assigned the ith object to the jth category. This must not contain any missing values and all the row totals must be equal.

Kappa takes the value one when there is complete agreement and zero when there is none (except that expected by chance). The printing of the test statistic and its associated information is controlled by the PRINT option. With the default, test, the procedure prints the actual and expected proportion of times that the judges agree, the resulting value of kappa and its variance. When N is large, the sampling distribution of kappa is approximately Normal. The procedure thus also prints the value of kappa divided by the variance, and its probability assuming a Normal distribution. A warning is printed if N is less than 20.

The STATISTIC and VARIANCE parameters allow kappa and its variance to be saved, in scalars.

Option: PRINT.



The method used is that of Siegel & Castellan (1988, pages 284-291).


Siegel, S. & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioural Sciences (second edition). McGraw-Hill, Singapore.

See also

Procedure: GSTATISTIC.

Commands for Basic and nonparametric statistics.


CAPTION 'KAPPA example',\ 
        'Data from Siegel & Castellan (1988) p. 287.'; STYLE=meta,plain
FACTOR  [LEVELS=29] Object
FACTOR  [LEVELS=5] Category
TABLE   [CLASSIFICATION=Object,Category;\ 
        VALUES=(4(0),4, 2,0,2,2(0))2, 3(0),1,3, 2(1),2,2(0), (3,0,1,2(0))2,\
        2(0),2(2),0, 3,0,1,2(0), 4(0),4, (4,4(0))3, 2(0),3,1,0, 1,0,2,1,0,\ 
        3(0),2(2), 4(0),4, 2(0),3,0,1, 0,1,3,2(0), 2(0),1,0,3, 2(0),3,1,0,\ 
        (4,4(0))2, 2,0,2,2(0), 1,0,3,2(0), (2,0,2,2(0))2, 0,1,2,0,1] Fish
KAPPA   Fish
Updated on March 7, 2019

Was this article helpful?