Fornell-Larcker AVE Calculator
Fornell–Larcker AVE loading squared sum/(sum+residual), independent standardized factor/unit measurement conventions; not reliability sum loadings². Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Average variance extracted compares squared loadings with total modeled item variance.
Average variance extracted compares squared loadings with total modeled item variance.1
It differs from reliability expressions based on the squared sum of loadings. The calculation and input requirements are explained below.
When to use Fornell-Larcker AVE Calculator
Enter nonempty aligned finite standardized loadings and nonnegative residual variances.
How to use Fornell-Larcker AVE 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 Fornell-Larcker AVE 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 |
|---|---|---|---|
| actualStandardizedLoadings | Actual Standardized Loadings. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
| actualResidualVariances | Actual Residual Variances. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter nonempty aligned finite standardized loadings and nonnegative residual variances. Require their squared-loading sum plus residual sum positive and return the squared-loading sum divided by that total. Model standardization is independently established.
AVE=sum(loadings^2)/(sum(loadings^2)+sum(residualVariances)) 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": {
"actualStandardizedLoadings": [
0.5,
0.5
],
"actualResidualVariances": [
0.25,
0.25
]
},
"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
-
Fornell-Larcker AVE 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.