Factor Communality From Loadings Calculator
Communality sum squared loadings only for independently established orthogonal/unit variance latent factors. Correlated factors require fullcovariance, not this model. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Communality sums squared loadings under orthogonal, unit-variance factor assumptions.
Communality sums squared loadings under orthogonal, unit-variance factor assumptions.1
Correlated factors require their covariance structure as well. The calculation and input requirements are explained below.
When to use Factor Communality From Loadings Calculator
Enter a nonempty finite loading list under orthogonal unit-variance factors.
How to use Factor Communality From Loadings 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 Factor Communality From Loadings 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 |
|---|---|---|---|
| actualOrthogonalFactorLoadings | Actual Orthogonal Factor Loadings. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Enter a nonempty finite loading list under orthogonal unit-variance factors. Sum squared loadings; the result is not capped at one. Correlated-factor covariance and model fit are not checked.
communality=sum(loadings^2) 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": {
"actualOrthogonalFactorLoadings": [
0.5,
0.5
]
},
"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 McDonald's Omega Total Reliability for a complementary operation: Unidimensional omega total compares common-factor variance with total variance.
Use Fornell-Larcker AVE Calculator for a complementary operation: Average variance extracted compares squared loadings with total modeled item variance.
References
Similar or alternative tools
- McDonald's Omega Total Reliability
Unidimensional omega total compares common-factor variance with total variance.
- Fornell-Larcker AVE Calculator
Average variance extracted compares squared loadings with total modeled item variance.