Koo and Li (2016) gives the following suggestion for interpreting ICC (Koo and Li 2016):

below 0.50: Poor

between 0.50 and 0.75: Moderate

between 0.75 and 0.90: Good

above 0.90: Excellent

\(x_{ij} = \mu + r_i + c_j + rc_{ij} + e_{ij} \)

\(\mu\): The population mean of observations

\(r_i\): The row effects (effects between each sample) are random, independent and normally distributed with mean 0 and variance \(\sigma_{r}^2\).

\(c_j\): The row effects (effects between each sample) are random, independent and normally distributed with mean 0 and variance \(\sigma_{c}^2\).

\(rc_{ij}\): Interaction effect, also random, independent and normally distributed with mean 0 and variance \(\sigma_{rc}^2\).

\(e_{ij}\): The residual error are random, independent and normally distributed with mean 0 and variance \(\sigma_{e}^2\). All residual effects are pairwise independent.

\(\frac{MS_R - MS_E}{MS_R + (k-1) MS_E + \frac{k}{n}(MS_c - MS_E)}\)

\(MS_R\) = mean square for rows;

\(MS_C\) = mean square for columns;

\(MS_E\) = mean square error;

k = number of measurements (number of columns);

n = number of object of measurement (number of rows);

`library("irr")`

`## Loading required package: lpSolve`

```
data("anxiety", package = "irr")
anxiety
```

```
## rater1 rater2 rater3
## 1 3 3 2
## 2 3 6 1
## 3 3 4 4
## 4 4 6 4
## 5 5 2 3
## 6 5 4 2
## 7 2 2 1
## 8 3 4 6
## 9 5 3 1
## 10 2 3 1
## 11 2 2 1
## 12 6 3 2
## 13 1 3 3
## 14 5 3 3
## 15 2 2 1
## 16 2 2 1
## 17 1 1 3
## 18 2 3 3
## 19 4 3 2
## 20 3 4 2
```

Use irr Library, need to specify model, type, unit:

```
icc(
anxiety, model = "twoway",
type = "agreement", unit = "single"
)
```

```
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 20
## Raters = 3
## ICC(A,1) = 0.198
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(19,39.7) = 1.83 , p = 0.0543
##
## 95%-Confidence Interval for ICC Population Values:
## -0.039 < ICC < 0.494
```

Use psych Library, calculate all type at once, for ICC(1,A), read from “Single_random_raters”:

```
library(psych)
ICC(anxiety)
```

```
## Call: ICC(x = anxiety)
##
## Intraclass correlation coefficients
## type ICC F df1 df2 p lower bound upper bound
## Single_raters_absolute ICC1 0.18 1.6 19 40 0.094 -0.0405 0.44
## Single_random_raters ICC2 0.20 1.8 19 38 0.056 -0.0045 0.45
## Single_fixed_raters ICC3 0.22 1.8 19 38 0.056 -0.0073 0.48
## Average_raters_absolute ICC1k 0.39 1.6 19 40 0.094 -0.1323 0.70
## Average_random_raters ICC2k 0.43 1.8 19 38 0.056 -0.0136 0.71
## Average_fixed_raters ICC3k 0.45 1.8 19 38 0.056 -0.0222 0.73
##
## Number of subjects = 20 Number of Judges = 3
```

Koo, Terry, and Mae Li. 2016. “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research.” Journal of Chiropractic Medicine 15 (March). doi:10.1016/j.jcm.2016.02.012.

Shrout, P.E., and J.L. Fleiss. 1979. “Intraclass Correlation: Uses in Assessing Rater Reliability.” Psychological Bulletin 86: 420–28.
McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods. 1996;1:30–46. [Google Scholar]