# 14. HTMT / HTMT+

## Purpose

Are the five customer-experience constructs empirically distinct?

## Practice study

- File: [customer-experience-pls.csv](../samples/customer-experience-pls.csv)
- Prepared project: [customer-experience-pls.qpls](../projects/customer-experience-pls.qpls)
- Observations: 400
- Data type: deterministic synthetic instructional data
- Complexity: a realistic multi-construct model with multi-item measurement blocks

## Exact procedure

1. Start QuickPLS and choose **Open Project**. Select [customer-experience-pls.qpls](../projects/customer-experience-pls.qpls). This prepared teaching project contains the exact dataset, analysis-ready base model, bindings, and a saved layout. Complete any method-specific term, group, or higher-order instruction stated below before calculation.

2. Open **Data** and confirm **customer-experience-pls.csv** contains 400 observations. Return to **Model**. The linked CSV remains available separately for inspection and re-import practice.

   ![HTMT / HTMT+ sample data](../screenshots/htmt/01-data-ready.png)

3. Confirm that the prepared model contains these constructs and measurement blocks: Quality (quality_1–quality_4), Value (value_1–value_3), Trust (trust_1–trust_4), Satisfaction (satisfaction_1–satisfaction_4), and Loyalty (loyalty_1–loyalty_4), all reflective.

   Confirm these saved structural paths: Quality, Value, and Trust → Satisfaction; Quality, Value, and Trust → Loyalty. This direct-effects teaching model deliberately leaves indirect-path analysis to the separate Mediation tutorial.

4. The prepared project already contains a saved, neatly arranged model layout. Do not choose **Arrange** or otherwise change the canvas before this tutorial calculation. If you later edit the model, arrange it once and save the edited project before calculating.

   ![HTMT / HTMT+ prepared model or variable roles](../screenshots/htmt/02-arranged-model.png)

5. Choose **Validate** and confirm there are no blockers. Do not save the unchanged prepared project; save only after you intentionally edit it.

6. Run **Calculate → PLS-SEM Algorithm** and keep **Open Results when finished** selected. The qualified 2.62.8 PLS result includes the point-estimate **HTMT+** and **HTMT — original signed correlations** tables. The screenshot below also identifies the separate **Discriminant Validity — HTMT/HTMT+** setup screen, but do not use that standalone route for this 2.62.8 teaching workflow; its saved-result publication was not release-qualified for this prepared project.

   ![HTMT / HTMT+ calculation setup](../screenshots/htmt/03-calculation-setup.png)

7. Use the ordinary PLS point-estimate settings. Read **Readiness** and choose **Start calculation** once. Wait for **Completed**; do not close the project while the result is being saved.

8. In **Results**, keep the qualified **PLS-SEM results** selected and inspect these outputs in order:

1. HTMT+ matrix
2. Original signed HTMT matrix
3. Heatmaps

   ![HTMT / HTMT+ primary result](../screenshots/htmt/04-results-primary.png)

   ![HTMT / HTMT+ secondary result](../screenshots/htmt/05-results-secondary.png)

9. Choose **Save Report** to preserve the exact result. Use **Export** for the needed table/report format. Close and reopen the project once and confirm the saved result remains selectable.

## Reading guidance

- The data are deterministic synthetic teaching data. They are suitable for reproducing the workflow, not for substantive publication claims.
- HTMT applies only to eligible multi-item reflective construct pairs. QuickPLS displays inapplicable formative or single-indicator cells as **N/A** with an explanation.
- This 2.62.8 tutorial verifies point matrices embedded in the qualified PLS result. It does not claim successful standalone HTMT bootstrap inference. Rerun that route only in a later release whose saved-result publication has been independently qualified.