Spearman-Brown Split-Half Reliability Calculator
Spearman–Brown equal-half reliability correction2r/(1+r),−1<r≤1, actual same instrument split. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
The Spearman–Brown equal-half correction transforms a split-half correlation.
The Spearman–Brown equal-half correction transforms a split-half correlation.1
Its reliability interpretation depends on the assumptions of the split and instrument. The calculation and input requirements are explained below.
When to use Spearman-Brown Split-Half Reliability Calculator
Enter split-half correlation greater than -1 and at most 1.
How to use Spearman-Brown Split-Half 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 Spearman-Brown Split-Half 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 |
|---|---|---|---|
| actualSplitHalfCorrelation | Actual Split Half Correlation. Use the units and matching context stated in the calculation below. | number | Required; finite; other constraints below |
Enter split-half correlation greater than -1 and at most 1. Return 2r/(1+r); negative corrected values are retained. The tool does not determine whether the equal-half assumptions apply.
correctedReliability=2*r/(1+r) 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": {
"actualSplitHalfCorrelation": 0.5
},
"output": {
"result": {
"value": 0.6666666666666666
}
}
}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 McDonald's Omega Total Reliability for a complementary operation: Unidimensional omega total compares common-factor variance with total variance.
References
Similar or alternative tools
- Standard Error of Measurement Calculator
Classical measurement error combines score variability and applicable reliability.
- McDonald's Omega Total Reliability
Unidimensional omega total compares common-factor variance with total variance.