kripp.boot: Bootstrap Krippendorff's Alpha

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/kripp.boot.R

Description

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.

Usage

1
2
kripp.boot(x, iter = 2000, probs = c(.025, .975), 
           method = c("nominal", "ordinal", "interval", "ratio"))

Arguments

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.

Value

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

Author(s)

Polina Proutskova (proutskova@googlemail.com)

Mike Gruszczynski (mikewgruz@gmail.com)

References

Krippendorff, K. (2011). Computing Krippendorff's Alpha-Reliability. Retrieved from http://repository.upenn.edu/asc_papers/43

See Also

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.

Examples

 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")

MikeGruz/kripp.boot documentation built on May 6, 2019, 3:27 p.m.