# 9. Permutation / Structural Path Randomization

## Purpose

Is the Leadership → Commitment path stronger than expected under structural-path randomization?

## Practice study

- File: [leadership-multigroup.csv](../samples/leadership-multigroup.csv)
- Prepared project: [leadership-multigroup.qpls](../projects/leadership-multigroup.qpls)
- Observations: 480
- 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 [leadership-multigroup.qpls](../projects/leadership-multigroup.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 **leadership-multigroup.csv** contains 480 observations. Return to **Model**. The linked CSV remains available separately for inspection and re-import practice.

   ![Permutation / Structural Path Randomization sample data](../screenshots/structural-path-randomization/01-data-ready.png)

3. Confirm that the prepared model contains these constructs and measurement blocks: Leadership (leadership_1–leadership_4), Climate (climate_1–climate_4), Commitment (commitment_1–commitment_4), and Performance (performance_1–performance_3), all reflective.

   Confirm these saved structural paths: Leadership → Climate; Leadership → Commitment; Climate → Commitment; Climate → Performance; Commitment → Performance.

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.

   ![Permutation / Structural Path Randomization prepared model or variable roles](../screenshots/structural-path-randomization/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 → Structural Path Randomization**.

   ![Permutation / Structural Path Randomization calculation setup](../screenshots/structural-path-randomization/03-calculation-setup.png)

7. Complete the method-specific settings:

- Select Leadership → Commitment as the focal path
- Use a fixed seed and the tutorial permutation count

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 **Permutation / Structural Path Randomization** is selected and inspect these outputs in order:

1. Observed focal-path estimate
2. Randomized reference distribution
3. Permutation probability and accounting

   ![Permutation / Structural Path Randomization primary result](../screenshots/structural-path-randomization/04-results-primary.png)

   ![Permutation / Structural Path Randomization secondary result](../screenshots/structural-path-randomization/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.