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vera-data-cfa-pipelining

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更新日2026年5月16日 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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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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VeraSuperHub/stat-research-pipeline

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vera-data-binary-generating

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vera-data-binary-reviewing

VeraSuperHub/stat-research-pipeline

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Full CFA skill for measurement-model evaluation. Extends the initial CFA fit with reliability, convergent/discriminant validity, alternative-model comparison, modification-index review, subgroup invariance testing, and manuscript-ready methods/results. Trigger after vera-data-cfa-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

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vera-data-cfa-reviewing

VeraSuperHub/stat-research-pipeline

Runs the CFA pipeline: collect indicator structure, check basic identification and estimator requirements, fit a primary CFA model, and report fit indices plus standardized loadings with a recommendation block for full SEM analysis. Trigger when the user asks for CFA, confirmatory factor analysis, measurement model testing, latent factor validation, factor loading assessment, construct validity, scale validation, measurement invariance check, or psychometric evaluation. Does not cover full structural path modeling (use vera-data-path-reviewing) or longitudinal change modeling (use vera-data-growth-reviewing).

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vera-data-continuous-generating

VeraSuperHub/stat-research-pipeline

Server-side extension that completes the full analysis pipeline for continuous outcome variables after vera-data-continuous-reviewing has run. Adds remaining hypothesis tests (ANOVA, post-hoc, nonparametric), subgroup analysis with interaction tests and forest plots, full modeling (OLS with diagnostics, quantile regression, tree-based exploratory), and model comparison. Generates manuscript-ready methods.md and results.md with formatted tables, publication-quality figures, and references.bib. Applies output variation and code style variation for natural, non-repetitive output. Triggered after vera-data-continuous-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.

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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.