ICC Reliability Calculator
Explicit ICC(1,1) one way random single measurement ANOVA estimate, actual externally established mean squares and equal k; negative results retained, not choose another ICC form. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
ICC reliability has several design-specific forms.
ICC reliability has several design-specific forms.1
The one-way random, single-measure ICC(1,1) uses between- and within-subject mean squares with equal ratings per person. The calculation and input requirements are explained below.
When to use ICC Reliability Calculator
Enter nonnegative between- and within-subject mean squares and integer ratings per person from 2 to 1000000.
How to use ICC Reliability Calculator and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run ICC Reliability Calculator.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| betweenSubjectsMeanSquare | Between Subjects Mean Square. Use the units and matching context stated in the calculation below. | number | Required; nonnegative; finite; other constraints below |
| withinSubjectsMeanSquare | Within Subjects Mean Square. Use the units and matching context stated in the calculation below. | number | Required; nonnegative; finite; other constraints below |
| ratingsPerPerson | Ratings Per Person. Use the units and matching context stated in the calculation below. | number | Required; integer 1–1000000; finite; other constraints below |
Enter nonnegative between- and within-subject mean squares and integer ratings per person from 2 to 1000000. Return the ICC(1,1) formula and retain negative estimates. An all-zero denominator fails output validation rather than producing a valid ICC.
ICC11=(MSbetween-MSwithin)/(MSbetween+(k-1)*MSwithin) Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"betweenSubjectsMeanSquare": 3,
"withinSubjectsMeanSquare": 1,
"ratingsPerPerson": 3
},
"output": {
"result": {
"value": 0.4
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Standard Error of Measurement Calculator for a complementary operation: Classical measurement error combines score variability and applicable reliability.
Use Spearman-Brown Split-Half Reliability Calculator for a complementary operation: The Spearman–Brown equal-half correction transforms a split-half correlation.
References
-
ICC Reliability Calculator — tool.io
Similar or alternative tools
- Standard Error of Measurement Calculator
Classical measurement error combines score variability and applicable reliability.
- Spearman-Brown Split-Half Reliability Calculator
The Spearman–Brown equal-half correction transforms a split-half correlation.