# 3. Weighted PLS

## Purpose

Do service, fairness, and satisfaction predict retention after applying case weights?

## Practice study

- File: [weighted-customer-retention.csv](../samples/weighted-customer-retention.csv)
- Prepared project: [weighted-customer-retention.qpls](../projects/weighted-customer-retention.qpls)
- Observations: 360
- 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 [weighted-customer-retention.qpls](../projects/weighted-customer-retention.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 **weighted-customer-retention.csv** contains 360 observations. Return to **Model**. The linked CSV remains available separately for inspection and re-import practice.

   ![Weighted PLS sample data](../screenshots/weighted-pls/01-data-ready.png)

3. Confirm that the prepared model contains these constructs and measurement blocks: Service (service_1–service_4), Fairness (fairness_1–fairness_3), Satisfaction (satisfaction_1–satisfaction_4), and Retention (retention_1–retention_3), all reflective.

   Confirm these saved structural paths: Service → Satisfaction; Fairness → Satisfaction; Fairness → Retention; Satisfaction → Retention.

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.

   ![Weighted PLS prepared model or variable roles](../screenshots/weighted-pls/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. Use **Calculate → Weighted PLS**.

   ![Weighted PLS calculation setup](../screenshots/weighted-pls/03-calculation-setup.png)

7. Complete the method-specific settings:

- Choose case_weight as the positive numeric case-weight variable
- Keep the first-run weighting and convergence defaults

8. Read **Readiness**, leave **Open Results when finished** selected, and choose **Start calculation** once. Wait for **Completed**; do not close the project while the result is being saved.

9. In **Results**, confirm **Weighted PLS** is selected and inspect these outputs in order:

1. Weighted path coefficients
2. Weighted outer estimates
3. Weighting/accounting evidence
4. Graphical Output

   ![Weighted PLS primary result](../screenshots/weighted-pls/04-results-primary.png)

   ![Weighted PLS secondary result](../screenshots/weighted-pls/05-results-secondary.png)

10. 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.