Description Usage Arguments Value Author(s) References See Also Examples
This function implements Prof. Klaus Krippendorff's algorithm for bootstrapping the Krippendorff's alpha coefficient. It computes confidence values (reliability estimates) for the given probabilities.
1 2 | kripp.boot(x, iter = 2000, probs = c(.025, .975),
method = c("nominal", "ordinal", "interval", "ratio"))
|
x |
is a matrix with rows (in R: observations) corresponding to judges and columns (in R: variables) corresponding to rated objects. Should be numeric, with NAs for missing data. |
iter |
the number of iterations for bootstrapping. |
probs |
a vector of probabilities for which confidence values are computed. |
method |
the metric used to calculate the difference function. "nominal", "ordinal", "interval", and "ratio" are currently implemented. |
A list containing the following components:
$mean.alpha |
the mean value of all bootstrapped alpha replicates |
$alpha |
a vector of bootstrapped alphas |
$upper |
upper alpha value for given probabilities |
$lower |
lower alpha value for given probabilities |
$raters |
number of raters used in calculating alpha |
$iter |
number of bootstrap replications |
$probs |
vector of probabilities used |
$size |
number of items used in calculating alpha |
Polina Proutskova (proutskova@googlemail.com)
Mike Gruszczynski (mikewgruz@gmail.com)
Krippendorff, K. (2011). Computing Krippendorff's Alpha-Reliability. Retrieved from http://repository.upenn.edu/asc_papers/43
Algorithm for bootstrapping a distribution of alpha (http://www.afhayes.com/public/alphaboot.pdf)
Andrew F. Hayes's SPSS code (http://www.afhayes.com/public/kalpha.sps)
Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Sage.
1 2 3 4 5 6 7 8 9 10 11 12 | # Krippendorff's "C" data (2011, 2)
nmm<-matrix(c(1,1,NA,1,2,2,3,2,3,3,3,3,3,3,3,3,2,2,2,2,1,2,3,4,4,4,4,4,
1,1,2,1,2,2,2,2,NA,5,5,5,NA,NA,1,1,NA,NA,3,NA),nrow=4)
# assume default nominal classification with 2000 replicates
kripp.boot(nmm)
# nominal classification with 5000 replicates
kripp.boot(nmm, iter=5000)
# ordinal classification with 5000 replicates
kripp.boot(nmm, iter=5000, method="ordinal")
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