# 5. Principal Component Analysis

## Purpose

How many empirical components summarize the twelve academic-engagement items?

## Practice study

- File: [scale-development-pca.csv](../samples/scale-development-pca.csv)
- Observations: 360
- Data type: deterministic synthetic instructional data
- Complexity: a realistic multivariable observed-data analysis

> **Installed 2.62.8 qualification note:** the application currently blocks the reproducible saved-project workflow for this route. A newly imported dataset cannot open **Analyze** until it is saved; a model-free general-SEM project is rejected when reopened; and adding a temporary model allows PCA to calculate but the completed result is then rejected during saved-result reconciliation because its immutable Start witness has no exact scientific model identity. The dataset and procedure below are retained as preparation material, but result screenshots, reporting, export, and reopen evidence remain pending.

## Exact procedure

1. Start QuickPLS and choose **New Project**. Enter a readable project name such as **Principal Component Analysis Tutorial** and choose a local `.qpls` destination.

2. Open **Data**, choose **Import Data**, select **scale-development-pca.csv**, keep the detected header row, review the preview, and choose **Import**. Confirm 360 observations are available.

   ![Principal Component Analysis imported data](../screenshots/principal-component-analysis/01-data-ready.png)

3. This method does not require a latent-variable diagram. In its setup screen use these roles: Select behavioral_1–behavioral_4, emotional_1–emotional_4, and cognitive_1–cognitive_4; do not select case_id.

4. Review the selected outcome, predictors, groups, or analysis variables. Remove case_id and every unused teaching/reference column from the analysis selection.

   ![Principal Component Analysis prepared model or variable roles](../screenshots/principal-component-analysis/02-variable-roles.png)

5. Check that every required role is populated with a numeric variable and that outcome/predictor selections do not overlap.

6. Use **Calculate → Principal Component Analysis**.

   ![Principal Component Analysis calculation setup](../screenshots/principal-component-analysis/03-calculation-setup.png)

7. Complete the method-specific settings:

- Use the Kaiser rule for the first run
- Request scores only if you need a reusable component dataset

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 **Principal Component Analysis** is selected and inspect these outputs in order:

1. Explained and cumulative variance
2. Scree plot
3. Component loadings
4. Component scores

   ![Principal Component Analysis primary result](../screenshots/principal-component-analysis/04-results-primary.png)

   ![Principal Component Analysis secondary result](../screenshots/principal-component-analysis/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.