DMAR (version 1.0.0)

fleiss_kappa: Fleiss's Kappa for Inter-Rater Agreement Among Multiple Raters

Description

Computes Fleiss's (1971) kappa coefficient of agreement among \(m \ge 2\) raters who classify each of \(N\) subjects into one of \(k\) nominal categories. Returns the point estimate together with the asymptotic standard error and the Wald confidence interval, plus a test of \(H_0\!: \kappa = 0\).

Usage

fleiss_kappa(
  ratings,
  conf_level = 0.95,
  ci_method = c("wald", "percentile", "bca"),
  B = 10000L,
  seed = NULL
)

Value

A one-row data.frame (class dmar_tbl) with columns

kappa, se (asymptotic standard error of \(\hat\kappa_F\)

used for the interval), lower_limit, upper_limit,

z_value, p_value (Wald test of \(H_0\!: \kappa = 0\)),

n_subjects, n_raters (\(m\)), and

n_categories (\(k\)).

Arguments

ratings

A numeric \(N \times k\) matrix or data.frame: row \(i\) gives the counts of raters who assigned each of the \(k\) categories to subject \(i\). Each row must sum to the same value \(m\) (the common number of raters per subject).

conf_level

Confidence level for the interval (default 0.95).

ci_method

Interval method: "wald" (the default, the asymptotic interval from the Gwet (2008) linearization variance), "percentile" (bootstrap percentile), or "bca" (bootstrap bias-corrected and accelerated). The multirater kappa variance literature is unsettled, and Zapf, Castell, Morawietz, and Karch (2016) recommend bootstrap intervals in this setting: the subjects (rows) are resampled with replacement B times, kappa is recomputed on each resample, and the interval is read off the bootstrap distribution; the BCa variant additionally adjusts the quantile positions for median bias and acceleration (Efron & Tibshirani, 1993). The se, z_value, and p_value columns keep their asymptotic definitions under every ci_method; only the interval changes.

B

Number of bootstrap replications when ci_method is "percentile" or "bca" (default 10000; ignored for "wald").

seed

Optional integer seed for the bootstrap. The default NULL uses the current state of the random number generator; a supplied seed is set internally and the prior state restored on exit.

Author

Ken Kelley [email protected]

Details

For \(n_{ij}\) = the number of raters who assigned subject \(i\) to category \(j\), with \(\sum_j n_{ij} = m\) for every \(i\), define the marginal proportion of category \(j\) as \(p_j = \sum_i n_{ij} / (Nm)\), and the per-subject agreement $$P_i = \frac{1}{m(m-1)}\Bigl(\sum_j n_{ij}^2 - m\Bigr).$$ Then Fleiss's kappa is $$\hat\kappa_F = \frac{\bar P - P_e}{1 - P_e}, \qquad \bar P = \frac{1}{N}\sum_i P_i, \qquad P_e = \sum_j p_j^2.$$

Standard error. Two variances are involved, because the variance of \(\hat\kappa_F\) under \(H_0\!: \kappa = 0\) is not its variance at a nonzero value. The test of no agreement uses the null variance of Fleiss, Nee, and Landis (1979, Equation 12), who corrected the standard errors given in Fleiss (1971), $$\mathrm{Var}_0(\hat\kappa_F) = \frac{2\,\bigl(P_e + P_e^2 - 2\sum_j p_j^3\bigr)}{N\,m\,(m-1)\,(1-P_e)^2},$$ and the reported \(z\) statistic and p-value come from it. On the Fleiss (1971) Table 1 example below this gives \(z = 17.65\), matching irr::kappam.fleiss. The confidence interval instead uses the linearization variance of Gwet (2008, Section 6), which is consistent at the estimated \(\hat\kappa_F\): each subject \(i\) contributes an influence value \(\kappa_i^\ast\) (Gwet's Equations 34 and 35), and \(\mathrm{Var}(\hat\kappa_F) = \sum_i (\kappa_i^\ast - \hat\kappa_F)^2 / \{N(N-1)\}\), Gwet's Equation 33 with the sampling fraction set to zero. (Gwet derives the variance for the multiple-rater pi statistic, which is the same estimator as Fleiss's kappa.) The Wald confidence interval is \(\hat\kappa_F \pm z_{1-\alpha/2}\,\widehat{\mathrm{SE}}\), with the upper limit truncated at 1. Using the null variance for the interval would understate the standard error and give a spuriously narrow interval.

Bootstrap interval. The variance of multirater kappa is unsettled in the literature, and Zapf, Castell, Morawietz, and Karch (2016) recommend a bootstrap interval in this setting. With ci_method = "percentile" or "bca" the subjects (the rows of ratings) are resampled with replacement B times, kappa is recomputed on each resample, and the interval is read off the bootstrap distribution: the percentile interval takes the empirical quantiles, and the BCa interval adjusts the quantile positions for median bias and for acceleration (Efron & Tibshirani, 1993). Ask for it when \(N\) is small or \(\hat\kappa_F\) is near a boundary, where the Wald interval's coverage is least dependable. The se, z_value, and p_value columns keep their asymptotic definitions under every ci_method; only the interval changes. Bootstrap results vary from run to run; supply seed for reproducibility.

Fleiss's kappa is purely nominal (no weighting). For ordinal categories with two raters, use cohen_kappa with quadratic weights; for ordinal categories with three or more raters, an extension based on the intraclass correlation (icc) is more appropriate.

References

Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378--382.

Fleiss, J. L., Nee, J. C. M., & Landis, J. R. (1979). Large sample variance of kappa in the case of different sets of raters. Psychological Bulletin, 86(5), 974--977.

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, NY: Chapman & Hall/CRC.

Gwet, K. L. (2008). Computing inter-rater reliability and its variance in the presence of high agreement. British Journal of Mathematical and Statistical Psychology, 61(1), 29--48. tools:::Rd_expr_doi("10.1348/000711006X126600")

Zapf, A., Castell, S., Morawietz, L., & Karch, A. (2016). Measuring inter-rater reliability for nominal data: Which coefficients and confidence intervals are appropriate? BMC Medical Research Methodology, 16, 93. tools:::Rd_expr_doi("10.1186/s12874-016-0200-9")

See Also

cohen_kappa, icc

Other reliability: cohen_kappa(), diagnosis_agreement, icc(), reliability(), reliability_H(), reliability_alpha(), reliability_kr20(), reliability_omega(), reliability_omega_categorical()

Examples

# Fleiss (1971) Table 1 example: 30 subjects rated by 6 raters into
# 5 diagnostic categories (Depression, Personality Disorder,
# Schizophrenia, Neurosis, Other). Each row of `ratings` gives, for one
# subject, the count of raters who chose each category (rows sum to 6).
# kappa = 0.430, matching Fleiss (1971).
fleiss_1971 <- matrix(c(
  0, 0, 0, 6, 0,
  0, 3, 0, 0, 3,
  0, 1, 4, 0, 1,
  0, 0, 0, 0, 6,
  0, 3, 0, 3, 0,
  2, 0, 4, 0, 0,
  0, 0, 4, 0, 2,
  2, 0, 3, 1, 0,
  2, 0, 0, 4, 0,
  0, 0, 0, 0, 6,
  1, 0, 0, 5, 0,
  1, 1, 0, 4, 0,
  0, 3, 3, 0, 0,
  1, 0, 0, 5, 0,
  0, 2, 0, 3, 1,
  0, 0, 5, 0, 1,
  3, 0, 0, 1, 2,
  5, 1, 0, 0, 0,
  0, 2, 0, 4, 0,
  1, 0, 2, 0, 3,
  0, 0, 0, 0, 6,
  0, 1, 0, 5, 0,
  0, 2, 0, 1, 3,
  2, 0, 0, 4, 0,
  1, 0, 0, 4, 1,
  0, 5, 0, 1, 0,
  4, 0, 0, 0, 2,
  0, 2, 0, 4, 0,
  1, 0, 5, 0, 0,
  0, 0, 0, 0, 6
), nrow = 30, byrow = TRUE)
fleiss_kappa(fleiss_1971)

# A percentile bootstrap interval, which resamples the subjects (rows)
# with replacement and recomputes kappa on each resample. Compare its
# limits with the Wald interval above; z_value and p_value keep their
# asymptotic definitions. B = 2000 keeps the example quick; a reported
# interval deserves the default B = 10000.
fleiss_kappa(fleiss_1971, ci_method = "percentile", B = 2000,
             seed = 113)