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vera-data-cfa-pipelining

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UpdatedMay 16, 2026 at 23:54

Complete skill family for confirmatory factor analysis (CFA) and measurement model evaluation. Use when the user asks for CFA, latent factor validation, measurement models, factor loadings, convergent/discriminant validity, or measurement invariance. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

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VeraSuperHub/stat-research-pipeline

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SKILL.md

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name	vera-data-cfa-pipelining
description	Complete skill family for confirmatory factor analysis (CFA) and measurement model evaluation. Use when the user asks for CFA, latent factor validation, measurement models, factor loadings, convergent/discriminant validity, or measurement invariance. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

SEM CFA — Skill Family

Open-source skill.

This domain contains:

vera-data-cfa-reviewing for identification checks, estimator selection, and an initial CFA fit with interpreted fit indices and standardized loadings
vera-data-cfa-generating for reliability/validity, invariance, model comparison, and manuscript-ready methods/results

Primary engine: lavaan in R. Secondary Python support may use semopy, but R is the default path whenever full SEM features are needed.

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name	vera-data-cfa-pipelining
description	Complete skill family for confirmatory factor analysis (CFA) and measurement model evaluation. Use when the user asks for CFA, latent factor validation, measurement models, factor loadings, convergent/discriminant validity, or measurement invariance. Uses a two-step structure: testing for initial diagnostics, analyzing for full pipeline.

SEM CFA — Skill Family

Open-source skill.

This domain contains:

vera-data-cfa-reviewing for identification checks, estimator selection, and an initial CFA fit with interpreted fit indices and standardized loadings
vera-data-cfa-generating for reliability/validity, invariance, model comparison, and manuscript-ready methods/results

Primary engine: lavaan in R. Secondary Python support may use semopy, but R is the default path whenever full SEM features are needed.