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Intraclass Correlation Calculator

Work out intraclass correlation instantly with clear inputs, formula shown and shareable results.

About Intraclass Correlation Calculator

The intraclass correlation is the share of total variance attributable to genuine differences between subjects. Averaging across raters reduces the error term, which is why the average-rater ICC is always higher.

Formulas

Single-rater ICC

ICC = σ²_between / (σ²_between + σ²_within)

Average-rater ICC

ICC = kσ²_between / (kσ²_between + σ²_within)

Worked example

With the default values below, Intraclass Correlation Calculator returns ICC for a single rater of 0.80000000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Between-subject varianceminimum 0
12
Within-subject (error) varianceminimum 0
3
Number of ratersaccepts 1–100
3

Results

ICC for a single rater
0.80000000
ICC for the average of all raters
0.92307692
Interpretation
Good single-rater reliability
Total variance
15.000000

Step by step

  1. 1Values used
  2. 2Single-rater ICC
  3. 3Average-rater ICC
  4. 4ICC for a single rater= 0.80000000
  5. 5ICC for the average of all raters= 0.92307692
  6. 6Interpretation= Good single-rater reliability
  7. 7Total variance= 15.000000

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 8 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

For between 12, within 3 and 3 raters?

The single-rater ICC is 0.8 (good), rising to 0.923 for the average of three raters.

How do I read the benchmarks?

Below 0.5 is poor, 0.5 to 0.75 moderate, 0.75 to 0.9 good and above 0.9 excellent, following Koo and Li.

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