Calculator Form
Use comma-separated values. The calculator handles AVE, Fornell–Larcker, HTMT, charting, and exportable summaries.
Example Data Table
| Example item | Construct | Illustrative value | Purpose |
|---|---|---|---|
| Loading 1 | Satisfaction | 0.84 | Used in AVE estimation |
| Loading 2 | Satisfaction | 0.81 | Used in AVE estimation |
| Loading 3 | Satisfaction | 0.79 | Used in AVE estimation |
| Loading 1 | Loyalty | 0.88 | Used in AVE estimation |
| Loading 2 | Loyalty | 0.85 | Used in AVE estimation |
| Loading 3 | Loyalty | 0.83 | Used in AVE estimation |
| Latent correlation | Satisfaction ↔ Loyalty | 0.62 | Compared with both square roots of AVE |
| HTMT threshold | Research rule | 0.85 | Used for HTMT decision |
The default sample values inside the form reproduce a clean pass case for both major checks.
Formula Used
1) Average Variance Extracted
AVE = Σ(loading²) / k
Each standardized loading is squared, summed, and divided by the number of indicators in that construct.
2) Fornell–Larcker Criterion
√AVE of each construct > absolute latent correlation
If both constructs have square roots of AVE greater than their shared correlation magnitude, discriminant validity is supported by this rule.
3) HTMT Ratio
HTMT = mean(|heterotrait correlations|) / √( mean(|monotrait A|) × mean(|monotrait B|) )
The numerator uses correlations across constructs. The denominator uses within-construct indicator correlations. Smaller HTMT values indicate clearer construct separation.
4) Overall Decision Logic
This page reports each criterion separately, then combines them into an overall summary so you can identify where validity concerns begin.
How to Use This Calculator
- Enter two construct names for clearer output labels.
- Paste standardized loadings for each construct as comma-separated values.
- Enter the latent correlation between the two constructs.
- Paste within-construct indicator correlations for both constructs.
- Paste all across-construct indicator correlations for the HTMT numerator.
- Choose an HTMT threshold, commonly 0.85 or 0.90.
- Submit the form and review results above the form.
- Use the CSV and PDF buttons to export your summary.
Frequently Asked Questions
1) What does discriminant validity show?
It shows whether two constructs are empirically distinct. If indicators and latent relationships overlap too strongly, the model may not separate concepts well enough.
2) Why does this calculator use AVE?
AVE summarizes how much indicator variance a construct captures from its items. Its square root is commonly compared with construct correlations in the Fornell–Larcker test.
3) What is a good HTMT threshold?
Researchers often use 0.85 for stricter evidence and 0.90 for a more lenient rule. Your field, model purpose, and validation standard can influence the choice.
4) Can negative loadings be entered?
Yes. AVE squares each loading, so sign does not affect the AVE magnitude. Still, unexpected negative loadings may signal coding or model specification problems.
5) Why are absolute correlations used here?
Discriminant validity is about separation strength, not direction. Absolute values help compare overlap magnitude consistently when correlations happen to be negative.
6) What if my HTMT is missing?
That usually means one or more required correlation lists were empty or unusable. Enter both within-construct sets and the full across-construct set to calculate HTMT.
7) Should I rely on one criterion only?
Using more than one check is safer. Fornell–Larcker and HTMT can complement each other and reveal different types of measurement overlap.
8) Can this be used for publication work?
Yes, as a supporting calculation tool. You should still report assumptions, estimation method, measurement model details, and the exact threshold rationale in your manuscript.