# 38. CB-SEM

## Purpose

Does the recursive common-factor service-recovery model reproduce the covariance structure adequately?

## Practice study

- File: [service-recovery-cbsem.csv](../samples/service-recovery-cbsem.csv)
- Observations: 500
- Data type: deterministic synthetic instructional data
- Complexity: a realistic multi-construct model with multi-item measurement blocks

## Exact procedure

1. Start QuickPLS and choose **New Project**. Enter a readable project name such as **CB-SEM Tutorial** and choose a local `.qpls` destination.

2. Open **Data**, choose **Import Data**, select **service-recovery-cbsem.csv**, keep the detected header row, review the preview, and choose **Import**. Confirm 500 observations are available.

   ![CB-SEM imported data](../screenshots/cb-sem/01-data-ready.png)

3. Create the following measurement model: Recovery Quality, Perceived Justice, Satisfaction, and Loyalty, each reflective with four matching indicators.

   Add these structural paths: Recovery Quality → Perceived Justice; Recovery Quality and Perceived Justice → Satisfaction; Recovery Quality and Satisfaction → Loyalty.

4. Choose **Arrange** once after completing the model, review the diagram, and save the project. Do not change the canvas again before this tutorial calculation.

   ![CB-SEM prepared model or variable roles](../screenshots/cb-sem/02-arranged-model.png)

5. Choose **Validate** and resolve every blocker. Save once after the model is ready.

6. Use **Calculate → CB-SEM / CFA → Recursive CB-SEM point mode**.

   ![CB-SEM calculation setup](../screenshots/cb-sem/03-calculation-setup.png)

7. Complete the method-specific settings:

- Use raw continuous data and the qualified maximum-likelihood point route
- Keep identification and convergence defaults for the first run

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 **CB-SEM** is selected and inspect these outputs in order:

1. Fit indices
2. Parameter estimates
3. Standardized solution
4. Residuals
5. Model diagram

   ![CB-SEM primary result](../screenshots/cb-sem/04-results-primary.png)

   ![CB-SEM secondary result](../screenshots/cb-sem/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.