# QuickPLS 2.62.7 method-specific tutorials

This package contains realistic, deterministic teaching studies and exact method-specific procedures for all 41 supported method entries. QuickPLS itself remains frozen at the released `v2.62.7` source and binary.

> **Evidence status:** the content/data gate and the complete PLS-SEM Bootstrapping installed-screenshot sequence pass. The remaining method screenshots are still being captured from the frozen 2.62.7 application. See [Evidence status](EVIDENCE_STATUS.md) before publishing these tutorials.

## How to use the package

1. Choose a method below.
2. Download or open its linked CSV from the `samples` folder.
3. Follow its exact model or variable-role definition.
4. Prepared projects already include a saved arranged SEM diagram. Open and calculate from that saved state; do not choose **Arrange** during the tutorial.
5. Run the calculation and compare the named result tables—not the exact sample estimates—with the screenshots.

## Supported methods

| # | Method | Practice data | N | Tutorial |
|---:|---|---|---:|---|
| 1 | PLS-SEM Algorithm | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/01-pls-sem-algorithm.md) |
| 2 | PLS power analysis | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/02-pls-power-analysis.md) |
| 3 | Weighted PLS | [weighted-customer-retention.csv](samples/weighted-customer-retention.csv) | 360 | [Open tutorial](methods/03-weighted-pls.md) |
| 4 | Consistent PLS | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/04-consistent-pls.md) |
| 5 | Principal Component Analysis | [scale-development-pca.csv](samples/scale-development-pca.csv) | 360 | [Open tutorial](methods/05-principal-component-analysis.md) |
| 6 | PLS-SEM Bootstrapping | [brand-choice-bootstrap.csv](samples/brand-choice-bootstrap.csv) | 420 | [Open tutorial](methods/06-pls-sem-bootstrapping.md) |
| 7 | PLSc Consistent Bootstrapping | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/07-plsc-consistent-bootstrapping.md) |
| 8 | Blindfolding | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/08-blindfolding.md) |
| 9 | Permutation / Structural Path Randomization | [leadership-multigroup.csv](samples/leadership-multigroup.csv) | 480 | [Open tutorial](methods/09-structural-path-randomization.md) |
| 10 | PLSc Consistent Permutation | [leadership-multigroup.csv](samples/leadership-multigroup.csv) | 480 | [Open tutorial](methods/10-plsc-consistent-permutation.md) |
| 11 | CVPAT | [loyalty-prediction.csv](samples/loyalty-prediction.csv) | 420 | [Open tutorial](methods/11-cvpat.md) |
| 12 | Confirmatory Composite Analysis | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/12-confirmatory-composite-analysis.md) |
| 13 | Confirmatory Tetrad Analysis | [innovation-tetrad.csv](samples/innovation-tetrad.csv) | 320 | [Open tutorial](methods/13-confirmatory-tetrad-analysis.md) |
| 14 | HTMT / HTMT+ | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/14-htmt.md) |
| 15 | Global GoF | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/15-global-gof.md) |
| 16 | PLS Model Fit | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/16-pls-model-fit.md) |
| 17 | PLS Model Comparison | [competing-loyalty-models.csv](samples/competing-loyalty-models.csv) | 450 | [Open tutorial](methods/17-pls-model-comparison.md) |
| 18 | Prediction-Oriented Model Selection | [competing-loyalty-models.csv](samples/competing-loyalty-models.csv) | 450 | [Open tutorial](methods/18-prediction-oriented-model-selection.md) |
| 19 | MICOM | [leadership-multigroup.csv](samples/leadership-multigroup.csv) | 480 | [Open tutorial](methods/19-micom.md) |
| 20 | PLS-MGA | [leadership-multigroup.csv](samples/leadership-multigroup.csv) | 480 | [Open tutorial](methods/20-pls-mga.md) |
| 21 | PLSc-MGA | [leadership-multigroup.csv](samples/leadership-multigroup.csv) | 480 | [Open tutorial](methods/21-plsc-mga.md) |
| 22 | PLSpredict | [loyalty-prediction.csv](samples/loyalty-prediction.csv) | 420 | [Open tutorial](methods/22-plspredict.md) |
| 23 | PLS-POS | [subscription-segmentation.csv](samples/subscription-segmentation.csv) | 600 | [Open tutorial](methods/23-pls-pos.md) |
| 24 | FIMIX-PLS | [subscription-segmentation.csv](samples/subscription-segmentation.csv) | 600 | [Open tutorial](methods/24-fimix-pls.md) |
| 25 | IPMA | [customer-experience-pls.csv](samples/customer-experience-pls.csv) | 400 | [Open tutorial](methods/25-ipma.md) |
| 26 | Moderation | [work-design-moderation.csv](samples/work-design-moderation.csv) | 450 | [Open tutorial](methods/26-moderation.md) |
| 27 | Mediation | [digital-training-mediation.csv](samples/digital-training-mediation.csv) | 420 | [Open tutorial](methods/27-mediation.md) |
| 28 | Nonlinear relationships | [digital-adoption-nonlinear.csv](samples/digital-adoption-nonlinear.csv) | 450 | [Open tutorial](methods/28-nonlinear-relationships.md) |
| 29 | Higher-order models | [service-quality-higher-order.csv](samples/service-quality-higher-order.csv) | 480 | [Open tutorial](methods/29-higher-order-models.md) |
| 30 | Gaussian-copula endogeneity | [trust-endogeneity.csv](samples/trust-endogeneity.csv) | 420 | [Open tutorial](methods/30-gaussian-copula-endogeneity.md) |
| 31 | GSCA | [supply-chain-component-model.csv](samples/supply-chain-component-model.csv) | 360 | [Open tutorial](methods/31-gsca.md) |
| 32 | Binary logistic regression | [business-outcomes-observed.csv](samples/business-outcomes-observed.csv) | 500 | [Open tutorial](methods/32-binary-logistic-regression.md) |
| 33 | Necessary Condition Analysis | [innovation-necessity.csv](samples/innovation-necessity.csv) | 360 | [Open tutorial](methods/33-necessary-condition-analysis.md) |
| 34 | PROCESS/path analysis | [business-outcomes-observed.csv](samples/business-outcomes-observed.csv) | 500 | [Open tutorial](methods/34-process-path-analysis.md) |
| 35 | PROCESS bootstrapping | [business-outcomes-observed.csv](samples/business-outcomes-observed.csv) | 500 | [Open tutorial](methods/35-process-bootstrapping.md) |
| 36 | OLS regression | [business-outcomes-observed.csv](samples/business-outcomes-observed.csv) | 500 | [Open tutorial](methods/36-ols-regression.md) |
| 37 | Regression bootstrapping | [business-outcomes-observed.csv](samples/business-outcomes-observed.csv) | 500 | [Open tutorial](methods/37-regression-bootstrapping.md) |
| 38 | CB-SEM | [service-recovery-cbsem.csv](samples/service-recovery-cbsem.csv) | 500 | [Open tutorial](methods/38-cb-sem.md) |
| 39 | CB-SEM bootstrapping | [service-recovery-cbsem.csv](samples/service-recovery-cbsem.csv) | 500 | [Open tutorial](methods/39-cb-sem-bootstrapping.md) |
| 40 | CFA | [student-wellbeing-cfa.csv](samples/student-wellbeing-cfa.csv) | 500 | [Open tutorial](methods/40-cfa.md) |
| 41 | PCA for CB-SEM preparation | [student-wellbeing-cfa.csv](samples/student-wellbeing-cfa.csv) | 500 | [Open tutorial](methods/41-pca-for-cb-sem-preparation.md) |

## Explicitly unavailable capability cells

- **CB-SEM Model Comparison:** No qualified Standard execution route is available in 2.62.7.
- **CB-SEM Multigroup Analysis:** No qualified Standard execution route is available in 2.62.7.
- **CB-SEM Measurement Invariance:** No qualified Standard execution route is available in 2.62.7.
- **CB-SEM Moderator Analysis:** No qualified Standard execution route is available in 2.62.7.
- **CB-SEM-specific PCA capability cell:** Use the ordinary Principal Component Analysis route for exploratory preparation; the separate CB-SEM capability cell remains unavailable.

## Evidence rules

- Screenshots must come from the exact installed QuickPLS 2.62.7 binary.
- A prepared-project diagram screenshot is accepted only when its saved layout opens cleanly and labels/arrows are visibly legible without invoking **Arrange** or another canvas command before calculation.
- Each calculation screenshot must show the selected method and required settings.
- Each Results screenshot must show the selected result plus the named table or chart.
- Synthetic data are labelled as instructional and are never presented as research validation.