# kendall.u: Kendall's Coefficient of Agreement In eba: Elimination-by-Aspects Models

## Description

Kendall's u coefficient of agreement between judges.

## Usage

 `1` ```kendall.u(M, cont.correct = FALSE) ```

## Arguments

 `M` a square matrix or a data frame consisting of absolute choice frequencies; row stimuli are chosen over column stimuli `cont.correct` logical, if TRUE a correction for continuity is applied (by deducting 1 from `chi2`), default is FALSE

## Details

Kendall's u takes values between `min.u` (when agreement is minimum) and 1 (when agreement is maximum). The minimum `min.u` equals -1/(m - 1), if m is even, and -1/m, if m is odd, where m is the number of subjects (judges).

The null hypothesis in the chi-square test is that the agreement between judges is by chance.

It is assumed that there is an equal number of observations per pair and that each subject judges each pair only once.

## Value

 `u` Kendall's u coefficient of agreement `min.u` the minimum value for u `chi2` the chi-square statistic for a test that the agreement is by chance `df` the degrees of freedom `pval` the p-value of the test

## References

Kendall, M.G., & Smith, B.B. (1940). On the method of paired comparisons. Biometrika, 31, 324–345.

`eba`, `strans`, `circular`.

## Examples

 ```1 2``` ```data(celebrities) kendall.u(celebrities) # moderate agreement ```

### Example output

```Kendall's u coefficient of agreement

u = 0.1171759, minimum u = -0.004291845
chi2 = 1027.812, df = 36, p-value = 0
alternative hypothesis: between-judges agreement is not by chance
correction for continuity has not been applied
```

eba documentation built on May 29, 2017, 4:26 p.m.