ICC Calculator

Compute the ICC(2,1) intraclass correlation coefficient from your ANOVA mean squares to judge how reliable a repeated fitness measurement really is.

Quick Facts

Method
ICC(2,1) = (MSR − MSE) / (MSR + (k−1)·MSE + (k/n)(MSC − MSE))
Two-way random-effects, single-rater, absolute-agreement (Shrout & Fleiss, 1979).

Your Results

Calculated
ICC(2,1)
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Single-measurement reliability
Reliability grade
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Koo & Li (2016) benchmark
ICC(2,k)
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Average of k measurements

Ready

Enter your ANOVA mean squares and click Calculate.

What the intraclass correlation coefficient measures

The intraclass correlation coefficient (ICC) tells you how much of the variation in a repeated measurement is real, subject-to-subject variation versus how much is measurement noise. In sport and exercise science it is the standard way to report the test-retest reliability of a fitness assessment (does the same athlete get the same score on two different days?) and the inter-rater reliability of a rated test (do two coaches score the same jump the same way?). The ICC ranges from 0 (all the spread in your data is measurement error) to 1 (measurements are perfectly repeatable).

The formula this calculator uses

This tool computes ICC(2,1) — the two-way random-effects, single-rater, absolute-agreement coefficient from Shrout & Fleiss (1979), equivalent to ICC(A,1) in McGraw & Wong (1996). You run a two-way ANOVA on your ratings matrix (subjects in rows, repeated measurements or raters in columns), read off the three mean squares, and plug them in:

ICC(2,1) = (MSR − MSE) / [ MSR + (k−1)·MSE + (k/n)·(MSC − MSE) ]

  • MSR — mean square for rows (between-subjects). Large when athletes genuinely differ from one another.
  • MSC — mean square for columns (between-measurements/raters). Large when one testing session or one rater is systematically higher or lower than another; this systematic bias is what pulls an absolute-agreement ICC down.
  • MSE — residual mean square (error). Random, unexplained noise.
  • n — number of subjects (rows); k — number of measurements or raters (columns).

To report the reliability of the average of all k measurements rather than a single one, use the average-measures form ICC(2,k) = (MSR − MSE) / [ MSR + (MSC − MSE)/n ], which this calculator also returns. Averaging always raises reliability, so ICC(2,k) is never below ICC(2,1).

Interpreting the value

The most widely cited benchmarks come from Koo & Li (2016):

  • Below 0.50 — poor reliability; the measurement is too noisy to track individual change.
  • 0.50 to 0.75 — moderate reliability.
  • 0.75 to 0.90 — good reliability; acceptable for most applied testing.
  • Above 0.90 — excellent reliability; suitable for clinical decisions and detecting small individual changes.

Report the ICC together with its 95% confidence interval whenever possible: a point estimate of 0.85 from only 8 subjects can have a lower bound below 0.50, which changes how much you should trust it.

Frequently Asked Questions

What does the ICC value mean?
The ICC ranges from 0 to 1 and gives the proportion of total variance attributable to true differences between subjects rather than to measurement error. Using the Koo & Li (2016) benchmark, values below 0.50 indicate poor reliability, 0.50–0.75 moderate, 0.75–0.90 good, and above 0.90 excellent reliability.
Which ICC form does this calculator compute?
It computes ICC(2,1), the two-way random-effects, single-rater, absolute-agreement coefficient from Shrout & Fleiss (1979). It also reports the average-measures ICC(2,k). Enter the ANOVA mean squares for subjects (MSR), measurements/raters (MSC) and residual error (MSE), plus the number of subjects (n) and measurements (k).
What is the difference between ICC(2,1) and ICC(3,1)?
Both are two-way models, but ICC(2,1) treats the raters/sessions as a random sample and measures absolute agreement, so systematic differences between sessions (the MSC term) lower the score. ICC(3,1) is a mixed model measuring consistency: it ignores that systematic bias and answers only whether scores rise and fall together. Use ICC(2,1) for test-retest reliability where the actual value must be reproducible.
Why can an ICC be negative?
When MSE exceeds MSR the formula can return a small negative number, meaning the estimated between-subjects variance is essentially zero and the measurement carries no reliable signal. Negative estimates are conventionally reported as 0, which is what this calculator does.

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