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grad-pls-sem

grad-pls-sem

github.com/asgard-ai-platformData & databases24347 installsUpdated 21w ago
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About grad-pls-sem

A PLS-SEM methodology skill for researchers using variance-based structural equation models. It guides measurement-model assessment and structural evaluation, emphasizing prediction and explained variance rather than overall model-fit testing.

Key features

  1. 01Reflective and formative model specification
  2. 02Assessment of reflective measurement quality
  3. 03Bootstrapping and VIF checks for formative measures
  4. 04Structural paths, R², f², and Q² evaluation
  5. 05Reporting guidance for PLS-SEM results

Use cases

  1. 01Assessing a model with formative constructs
  2. 02Analyzing a complex structural model with limited sample size
  3. 03Evaluating a model focused on prediction and variance explanation

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/asgard-ai-platform/skills --skill grad-pls-sem

After installing, type/grad-pls-semin Claude Code to confirm it loaded.

Bundled filesexamples/sample_scenario.md

Details

From the project README

PLS-SEM 偏最小平方法結構方程模型

Overview

PLS-SEM (Wold, 1982; Hair et al., 2017) is a variance-based approach to structural equation modeling that estimates composite-based path models. Unlike CB-SEM, it maximizes explained variance in endogenous constructs and readily handles both reflective and formative measurement models.

When to Use

  • Formative measurement models are part of the research design
  • Sample size is small (PLS works with N ≥ 10× the largest number of paths pointing to any construct)
  • Research goal is prediction and variance explanation rather than theory confirmation
  • The structural model is complex with many constructs and indicators

When NOT to Use

  • Research goal is strict theory testing and model fit assessment
  • All constructs are reflective and sample size is adequate for CB-SEM
  • You need global model fit indices (chi-square, CFI, RMSEA)
  • Circular relationships (non-recursive models) are hypothesized

Assumptions

IRON LAW: PLS-SEM maximizes VARIANCE EXPLAINED, not model fit — it does NOT
test overall model fit like CB-SEM. A high R² does not mean the model
structure is correct.

Key assumptions:

  1. Predictor specification — each construct must be correctly specified as reflective or formative
  2. No circular (non-recursive) relationships in the structural model
  3. Observations are independent (no nested structure without extensions)
  4. Data need not be normally distributed (PLS is distribution-free)

Methodology

Step 1 — Specify Measurement Models

Classify each construct as reflective (arrows from construct to indicators) or formative (arrows from indicators to construct). Formative constructs require at minimum two indicators and a theoretical rationale.

Step 2 — Assess Reflective Measurement

Evaluate indicator reliability (loadings ≥ 0.70), internal consistency (CR ≥ 0.70), convergent validity (AVE ≥ 0.50), and discriminant validity (HTMT < 0.90).

Step 3 — Assess Formative Measurement

Check indicator weights for significance via bootstrapping. Examine VIF among indicators (VIF < 5.0). Assess content validity — dropping a formative indicator changes the construct meaning.

Step 4 — Evaluate Structural Model

Report path coefficients, R², f² effect sizes, Q² predictive relevance (via blindfolding), and bootstrapped confidence intervals. See references/ for algorithm details.

Output Format

## PLS-SEM Analysis: [Study Title]

### Reflective Measurement Assessment
| Construct | Indicator | Loading | CR | AVE | HTMT |
|-----------|-----------|---------|-----|-----|------|
| [name] | [item] | x.xx | x.xx | x.xx | x.xx |

### Formative Measurement Assessment
| Construct | Indicator | Weight | VIF | p-value |
|-----------|-----------|--------|-----|---------|
| [name] | [item] | x.xx | x.xx | x.xx |

### Structural Model
| Path | β | t-value | p-value | f² | Supported? |
|------|---|---------|---------|-----|------------|
| X → Y | x.xx | x.xx | x.xx | x.xx | [Yes/No] |

### Model Quality
| Endogenous Construct | R² | Q² |
|---------------------|-----|-----|
| [name] | x.xx | x.xx |

### Limitations
- [Note any assumption violations]

Gotchas

  • PLS-SEM is NOT a silver bullet for small samples — it still requires adequate statistical power
  • Misspecifying reflective as formative (or vice versa) fundamentally changes results
  • HTMT is preferred over Fornell-Larcker for discriminant validity in PLS-SEM
  • PLS overestimates loadings and underestimates path coefficients (consistency at large corrects this)
  • Blindfolding Q² > 0 shows predictive relevance but does not validate the model structure
  • Reporting PLS results using CB-SEM criteria (CFI, RMSEA) is methodologically incorrect

References

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (2nd ed.). Sage.
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity. Journal of the Academy of Marketing Science, 43(1), 115-135.
  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report PLS-SEM. European Business Review, 31(1), 2-24.

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