Which ICC model should I use?

ICC(1) suits one-way designs where each subject is rated by a different set of randomly selected raters. ICC(2) suits two-way random designs where the same raters rate all subjects and raters are a random sample from a larger pool. ICC(3) suits two-way mixed designs where raters are the specific raters of interest (not a random sample).

What is the difference between Single and Average?

Single (ICC k,1) reflects the reliability of a single rater's measurement. Average (ICC k,k) uses the Spearman-Brown formula to reflect the reliability when the final score is the mean of all k raters. Average ICC is always higher and applies when you actually average raters.

What ICC value is acceptable?

Common thresholds: ICC 0.90 = excellent. These are guidelines — clinical measurement tools typically require ICC > 0.90 for interchangeable use.

How should I format my data?

Rows represent subjects (patients, items, trials); columns represent raters (judges, sessions, methods). Separate values with tabs — you can copy directly from Excel or Google Sheets. All cells must be filled; missing data is not supported.

What statistical test underlies the ICC?

ICC is derived from a mean-squares decomposition identical to a one-way or two-way ANOVA. The F-statistic tests whether subjects differ significantly from one another; a significant F (p < 0.05) indicates there is enough variance across subjects for ICC to be meaningful.

Yes. A negative ICC occurs when raters agree less than chance, for example when raters systematically disagree or when there is very little true variability across subjects relative to error. Treat a negative ICC as effectively zero.

Intraclass Correlation Calculator

Name: Intraclass Correlation Calculator
Availability: InStock
Author: Nham Vu

Paste a subjects-by-raters data matrix to compute ICC(1), ICC(2), and ICC(3) with confidence intervals, F-tests, and plain-English interpretation.

Data Matrix

Rows = subjects, columns = raters. Paste from Excel or Google Sheets (tab-separated).

ICC Model

Measurement

Model Selection Guide

ICC(1) Each subject rated by different random raters

ICC(2) Same raters rate all subjects; raters are a random sample

ICC(3) Same raters rate all subjects; raters are the raters of interest

Reliability Thresholds (Koo & Mae, 2016)

> 0.90 Excellent

0.75–0.90 Good

0.50–0.75 Moderate

< 0.50 Poor

Paste your data and click Calculate

ICC(2,k)

Subjects (n)

Raters (k)

F (subjects)

p-value

0.00.500.750.901.0

ANOVA Summary

Source	SS	df	MS

All ICC Estimates

Form	ICC	95% CI Lower	95% CI Upper

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Summary