# 16. PLS Model Fit

## Purpose

What descriptive model-fit diagnostics accompany the customer-experience PLS estimate?

## 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.

   ![PLS Model Fit sample data](../screenshots/pls-model-fit/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.

   ![PLS Model Fit prepared model or variable roles](../screenshots/pls-model-fit/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 → PLS-SEM Algorithm; Model Fit is produced automatically**.

   ![PLS Model Fit calculation setup](../screenshots/pls-model-fit/03-calculation-setup.png)

7. Complete the method-specific settings:

- Use the ordinary PLS point-estimate route

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

1. Model Fit — descriptive
2. SRMR and applicable discrepancy summaries
3. Method details and boundaries

   ![PLS Model Fit primary result](../screenshots/pls-model-fit/04-results-primary.png)

   ![PLS Model Fit secondary result](../screenshots/pls-model-fit/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.
- Treat SRMR, NFI, d_ULS, and d_G as descriptive PLS fit information. Exact-fit decisions require the adapted Bollen–Stine inference identified in the result notice; that inference is not part of this point-estimate workflow.