irr (version 0.84.1)

kappam.fleiss: Fleiss' Kappa for m raters

Description

Computes Fleiss' Kappa as an index of interrater agreement between m raters on categorical data. Additionally, category-wise Kappas could be computed.

Usage

kappam.fleiss(ratings, exact = FALSE, detail = FALSE)

Arguments

ratings

n*m matrix or dataframe, n subjects m raters.

exact

a logical indicating whether the exact Kappa (Conger, 1980) or the Kappa described by Fleiss (1971) should be computed.

detail

a logical indicating whether category-wise Kappas should be computed

Value

A list with class '"irrlist"' containing the following components:

$method

a character string describing the method applied for the computation of interrater reliability.

$subjects

the number of subjects examined.

$raters

the number of raters.

$irr.name

a character string specifying the name of the coefficient.

$value

value of Kappa.

$stat.name

a character string specifying the name of the corresponding test statistic.

$statistic

the value of the test statistic.

$p.value

the p-value for the test.

$detail

a table with category-wise kappas and the corresponding test statistics.

Details

Missing data are omitted in a listwise way. The coefficient described by Fleiss (1971) does not reduce to Cohen's Kappa (unweighted) for m=2 raters. Therefore, the exact Kappa coefficient, which is slightly higher in most cases, was proposed by Conger (1980). The null hypothesis Kappa=0 could only be tested using Fleiss' formulation of Kappa.

References

Conger, A.J. (1980). Integration and generalisation of Kappas for multiple raters. Psychological Bulletin, 88, 322-328. Fleiss, J.L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76, 378-382. Fleiss, J.L., Levin, B., & Paik, M.C. (2003). Statistical Methods for Rates and Proportions, 3rd Edition. New York: John Wiley & Sons.

See Also

kappa2, kappam.light

Examples

# NOT RUN {
data(diagnoses)
kappam.fleiss(diagnoses)               # Fleiss' Kappa
kappam.fleiss(diagnoses, exact=TRUE)   # Exact Kappa
kappam.fleiss(diagnoses, detail=TRUE)  # Fleiss' and category-wise Kappa

kappam.fleiss(diagnoses[,1:4])         # Fleiss' Kappa of raters 1 to 4
# }