Average Interitem Correlation Calculator
Mean independently computed distinct unordered offdiagonalinteritemPearsoncorrelations, diagonal/exclusions externally audited; not mean raw items. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Average interitem correlation summarizes distinct off-diagonal Pearson correlations.
Average interitem correlation summarizes distinct off-diagonal Pearson correlations.1
Diagonals and repeated pairs must be excluded before averaging. The calculation and input requirements are explained below.
When to use Average Interitem Correlation Calculator
Enter nonempty independently computed distinct off-diagonal correlations from -1 to 1.
How to use Average Interitem Correlation 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 Average Interitem Correlation 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 |
|---|---|---|---|
| actualDistinctOffDiagonalInteritemCorrelations | Actual Distinct Off Diagonal Interitem Correlations. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter nonempty independently computed distinct off-diagonal correlations from -1 to 1. Return their arithmetic mean. Pair uniqueness, diagonal exclusion and correlation estimation are not checked.
meanInteritemCorrelation=mean(distinctOffDiagonalCorrelations) 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": {
"actualDistinctOffDiagonalInteritemCorrelations": [
0.2,
0.8
]
},
"output": {
"result": {
"value": 0.5
}
}
}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
-
Average Interitem Correlation 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.