# N2.cohen.kappa: Sample Size Calculation for Cohen's Kappa Statistic with more... In irr: Various Coefficients of Interrater Reliability and Agreement

## Description

This function calculates the required sample size for the Cohen's Kappa statistic when two raters have the same marginal. Note that any value of "kappa under null" in the interval [-1,1] is acceptable (i.e. k0=0 is a valid null hypothesis).

## Usage

 1 N2.cohen.kappa(mrg, k1, k0, alpha=0.05, power=0.8, twosided=FALSE)

## Arguments

 mrg a vector of marginal probabilities given by raters k1 the true Cohen's Kappa statistic k0 the value of kappa under the null hypothesis alpha type I error of test power the desired power to detect the difference between true kappa and hypothetical kappa twosided TRUE if test is two-sided

## Value

Returns required sample size.

## Author(s)

Puspendra Singh and Jim Lemon

## References

Flack, V.F., Afifi, A.A., Lachenbruch, P.A., & Schouten, H.J.A. (1988). Sample size determinations for the two rater kappa statistic. Psychometrika, 53, 321-325.

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 require(lpSolve) # Testing H0: kappa = 0.4 vs. HA: kappa > 0.4 (=0.6) given that # Marginal Probabilities by two raters are (0.2, 0.25, 0.55). # # one sided test with 80% power: N2.cohen.kappa(c(0.2, 0.25, 0.55), k1=0.6, k0=0.4) # one sided test with 90% power: N2.cohen.kappa(c(0.2, 0.25, 0.55), k1=0.6, k0=0.4, power=0.9) # Marginal Probabilities by two raters are (0.2, 0.05, 0.2, 0.05, 0.2, 0.3) # Testing H0: kappa = 0.1 vs. HA: kappa > 0.1 (=0.5) given that # # one sided test with 80% power: N2.cohen.kappa(c(0.2, 0.05, 0.2, 0.05, 0.2, 0.3), k1=0.5, k0=0.1)