Efa Cfa Measurement Model
- 1 installs
- 52 repo stars
- Updated May 19, 2026
- drchronx/ai-agent-research-starter-kit
Plan, run, and report exploratory and confirmatory factor analysis for academic scales, including fit indices, loadings, and questionnaire validation.
About
This skill guides exploratory and confirmatory factor analysis for academic measurement models, covering data suitability, model specification, fit indices, and reporting. Researchers use it to validate questionnaires and evaluate latent-variable factor structures.
- Reports factor loadings, fit indices (CFI, TLI, RMSEA, SRMR), and model comparisons
- Enforces constraints against overfitting, chasing fit, or overclaiming construct validity
Efa Cfa Measurement Model by the numbers
- 1 all-time installs (skills.sh)
- Ranked #1,803 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 1 |
|---|---|
| repo stars | ★ 52 |
| Last updated | May 19, 2026 |
| Repository | drchronx/ai-agent-research-starter-kit ↗ |
What it does
Plan, run, and report exploratory and confirmatory factor analysis for academic scales, including fit indices, loadings, and questionnaire validation.
Files
EFA CFA Measurement Model
Use this skill for factor-structure evaluation.
Inputs
- Item-level data.
- Expected factor structure.
- Sample size and population.
- Software preference: R, Python, Mplus, lavaan, AMOS, jamovi, SPSS.
Workflow
1. Check data suitability: sample size, missingness, item distributions, correlations. 2. Use EFA only when structure is uncertain or being explored. 3. Use CFA when testing a theoretically specified measurement model. 4. Report factor loadings, fit indices, residual issues, and correlated errors only when justified. 5. Compare alternative models if discriminant validity is a concern. 6. Produce tables and a reporting paragraph.
Hard Constraints
- Do not use EFA and CFA on the same sample as if it were independent validation unless labeled exploratory.
- Do not add correlated errors only to chase fit.
- Do not delete items without theory and transparent reporting.
- Do not overclaim construct validity from a single CFA.
Output
Analysis choice:
Model specification:
Fit-index table:
Loading table:
Model comparison:
Modification risks:
Reporting paragraph:interface:
display_name: "EFA CFA Models"
short_description: "Plan and report factor models"
default_prompt: "Use $efa-cfa-measurement-model to plan EFA/CFA analysis for my scale data."
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