HTMT Calculator for Discriminant Validity
Calculate the heterotrait–monotrait ratio of correlations (Henseler, Ringle and Sarstedt, 2015), the recommended test of discriminant validity. Values below .85 (or .90) indicate that constructs are empirically distinct.
Updated · By Bibin XavierData 180 × 12
| Respondenttext · 180 levels | TR1numeric · coded | TR2numeric · coded | TR3numeric · coded | TR4numeric · coded | SAT1numeric · coded | SAT2numeric · coded | SAT3numeric · coded | SAT4numeric · coded | LOY1numeric · coded | LOY2numeric · coded | LOY3numeric · coded |
|---|---|---|---|---|---|---|---|---|---|---|---|
| R001 | 2 | 2 | 1 | 2 | 3 | 1 | 3 | 3 | 4 | 2 | 3 |
| R002 | 5 | 4 | 5 | 4 | 3 | 4 | 3 | 4 | 4 | 3 | 4 |
| R003 | 4 | 4 | 3 | 4 | 1 | 2 | 2 | 4 | 2 | 3 | 1 |
| R004 | 1 | 2 | 3 | 2 | 4 | 4 | 4 | 4 | 3 | 4 | 4 |
| R005 | 3 | 4 | 2 | 3 | 3 | 3 | 3 | 4 | 3 | 2 | 2 |
| R006 | 5 | 4 | 5 | 4 | 5 | 5 | 4 | 4 | 4 | 3 | 4 |
| R007 | 3 | 4 | 4 | 4 | 3 | 3 | 2 | 4 | 1 | 3 | 3 |
| R008 | 3 | 3 | 4 | 4 | 5 | 4 | 5 | 5 | 5 | 5 | 4 |
Variables and options
Trust =~ TR1 + TR2 + TR3.Results
180 rows · example dataConstruct reliability and convergent validity
| Construct | Items | Cronbach’s α | CR (ρc) | McDonald’s ω | AVE | √AVE | MSV | Verdict |
|---|---|---|---|---|---|---|---|---|
| TR | 4 | .838 | .838 | .838 | .565 | .751 | .332 | Supported |
| SAT | 4 | .816 | .817 | .818 | .529 | .727 | .432 | Supported |
| LOY | 3 | .846 | .846 | .846 | .646 | .804 | .432 | Supported |
Note. Thresholds (Hair et al., 2019; Fornell and Larcker, 1981): α, CR and ω ≥ .70 (≤ .95); AVE ≥ .50; MSV below AVE for discriminant validity. CR and AVE use standardized loadings; ω uses the unstandardized solution.
Fornell–Larcker criterion
| TR | SAT | LOY | |
|---|---|---|---|
| TR | .751 | ||
| SAT | .576 | .727 | |
| LOY | .516 | .657 | .804 |
Note. Diagonal: √AVE. Off-diagonal: latent correlations. Discriminant validity holds when each √AVE exceeds that construct’s correlations.
Heterotrait–monotrait ratio (HTMT)
| TR | SAT | LOY | |
|---|---|---|---|
| TR | — | ||
| SAT | .584 | — | |
| LOY | .510 | .648 | — |
Note. HTMT below .85 (strict) or .90 (liberal) supports discriminant validity (Henseler, Ringle and Sarstedt, 2015). Computed from absolute item correlations.
Standardized loadings
| Construct | Item | λ | λ² | p |
|---|---|---|---|---|
| TR | TR1 | .730 | .532 | fixed |
| TR | TR2 | .754 | .569 | < .001 |
| TR | TR3 | .749 | .562 | < .001 |
| TR | TR4 | .772 | .595 | < .001 |
| SAT | SAT1 | .723 | .523 | fixed |
| SAT | SAT2 | .771 | .594 | < .001 |
| SAT | SAT3 | .772 | .595 | < .001 |
| SAT | SAT4 | .636 | .405 | < .001 |
| LOY | LOY1 | .795 | .632 | fixed |
| LOY | LOY2 | .786 | .617 | < .001 |
| LOY | LOY3 | .830 | .689 | < .001 |
CFA model fit (for reference)
| Index | Value | Threshold | Verdict |
|---|---|---|---|
| χ² / df | 1.23 | ≤ 3 (≤ 5 acceptable) | Good |
| CFI | .989 | ≥ .95 (≥ .90 acceptable) | Good |
| TLI | .985 | ≥ .95 (≥ .90 acceptable) | Good |
| RMSEA | .036 [.000, .065] | ≤ .06 (≤ .08 acceptable) | Good |
| SRMR | .037 | ≤ .08 | Good |
| p-close (RMSEA ≤ .05) | .755 | > .05 | Good |
| GFI | .952 | ≥ .90 | Good |
| AIC / BIC | 4986.9 / 5066.7 | lower is better (compare models) | — |
Note. Thresholds follow Hu and Bentler (1999). With small samples or many indicators, judge fit on several indices together.
Interpretation
All 3 constructs meet the thresholds for reliability (CR ≥ .70) and convergent validity (AVE ≥ .50). Discriminant validity is supported by both the Fornell–Larcker criterion and HTMT.
How to cite this tool
Using these results in a thesis, journal article, report or class assignment? Please cite this free tool so others can find it. It keeps the site free.
APA 7
Xavier, B. (2026). HTMT Calculator for Discriminant Validity [Web application]. FreeStatisticalTools.com. https://freestatisticaltools.com/htmt-calculator/
BibTeX
@software{xavier_htmtcalculator_2026,
author = {Xavier, Bibin},
title = {HTMT Calculator for Discriminant Validity},
publisher = {FreeStatisticalTools.com},
year = {2026},
url = {https://freestatisticaltools.com/htmt-calculator/},
note = {Web application}
}Suggested sentence for your methods section: “The analysis was carried out with the free HTMT Calculator for Discriminant Validity tool on FreeStatisticalTools.com (Xavier, 2026).”
What is the HTMT ratio?
The heterotrait–monotrait ratio of correlations compares the average correlation between items of different constructs with the average correlation between items of the same construct. Values close to 1 mean two constructs are not empirically distinct.
When to use the HTMT ratio
Use it in the measurement-model section of any survey-based study to establish reliability, convergent validity and discriminant validity before testing hypotheses.
Assumptions
- Reflective constructs with at least two, preferably three or more items.
- Items keyed in the same direction (reverse-code first).
How to use this tool
- Enter your data. Paste columns from Excel, Google Sheets or SPSS with variable names in the first row, or upload a CSV or Excel file. The example data shows the expected layout.
- Choose the variables. Select the constructs and their items.
- Read the results. Results update automatically. Read the plain-English interpretation, then use Copy table to paste APA-style tables into Word and Copy for the results sentence.
How to report the HTMT ratio in APA style
Report a table with α, CR, AVE (and ω) for each construct, then a Fornell–Larcker or HTMT matrix.
Frequently asked questions
What is an acceptable HTMT value?
Below .85 under the strict criterion and below .90 under the liberal one (Henseler, Ringle and Sarstedt, 2015).
How is HTMT calculated?
HTMT = mean heterotrait-heteromethod correlation divided by the geometric mean of the average monotrait-heteromethod correlations of the two constructs.
What if HTMT is too high?
Check for items that load on both constructs, consider merging constructs that are conceptually close, or remove items with high cross-correlations.
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