vera-data-path-reviewing

name	vera-data-path-reviewing
description	Structural equation modeling skill. Collects latent constructs and path structure, checks identification and estimator requirements, fits one primary SEM, and reports global fit plus key structural paths with a recommendation block for advanced analyses. Trigger when the user asks for SEM, latent path analysis, full structural equation modeling, path coefficients, structural paths, mediation model, latent variable model, causal SEM, direct and indirect effects, or path diagram. Does not handle CFA-only models (use vera-data-cfa-reviewing) or longitudinal growth models (use vera-data-growth-reviewing).
allowed-tools	Read, Bash, Write, Edit

Full SEM Testing — Initial Structural Model

Table of Contents

• Scope Boundary
• Workflow
• Decision Tree
• Required Inputs
• Code Structure
• Reporting Standards
• Method Status
• Minimal Smoke Test
• Failure Modes

Open-source skill.

Scope Boundary

Use this skill when:

• The model includes both a measurement component and structural paths among latent and/or observed variables.
• A first-pass SEM fit is needed before indirect-effect bootstrapping, multigroup comparison, or model revision.

Do not use this skill when:

• The task is CFA-only without structural paths; use vera-data-cfa-reviewing.
• The core question is longitudinal growth / change; use vera-data-growth-reviewing.

Workflow

Read each step file in workflow/ before executing that step.

Step	Responsibility	Executor	Document	Input	Output
Collect	Collect Inputs	Main Agent	workflow/step01-collect-inputs.md	User input	Structured SEM summary
Diagnose	Check Model Setup	Main Agent	workflow/step02-check-model-setup.md	Prior step output	Identification + estimator decision
Test	Run Primary Sem	Main Agent	workflow/step03-run-primary-sem.md	Prior step output	Initial SEM fit + recommendation

Decision Tree

1. CHECK MEASUREMENT COMPONENT
   ├── latent factors specified → SEM path
   └── only observed variables → consider path analysis, but keep SEM framing explicit

2. CHECK STRUCTURAL COMPONENT
   ├── direct paths only → baseline SEM
   ├── mediation present → label indirect paths for later testing
   └── multigroup / longitudinal / nonlinear elements → recommend specialized SEM skills

3. CHECK ESTIMATOR
   ├── continuous indicators → ML / MLR
   └── categorical indicators → WLSMV / DWLS

Required Inputs

Role	What to collect
Latent constructs	Factor names, theoretical meaning, indicators per factor
Structural paths	Which latent/observed variables predict which
Mediators	Any indirect paths (X → M → Y)
Scale type	Continuous, ordinal/Likert, binary indicators
Grouping variable	Optional; for later multi-group comparison
Sample size	Final analytic N and missing-data context

Code Structure

PART 0: Setup & Data Loading
PART 1: Model Setup Checks (identification, estimator)
PART 2: Primary SEM Fit (global fit + structural coefficients)
PART 3: Recommendation Block

Reporting Standards

1. Always report chi-square, df, CFI, TLI, RMSEA, and SRMR
2. Report standardized path coefficients with SE or CI
3. Report R-squared for endogenous latent variables
4. Identification problems must be stated explicitly, never silently patched
5. Mediation paths: label but do not test significance (recommend full analysis)
6. Ends with recommendation block for mediation testing, multi-group analysis, model modification, and indirect effect bootstrapping
7. Fit-index thresholds: CFI / TLI ≥ 0.90 (≥ 0.95 preferred); RMSEA ≤ 0.08 (≤ 0.06 preferred); SRMR ≤ 0.08. For ordinal/categorical indicators with DWLS/WLSMV, report robust variants.

Method Status

Status	Methods
Implemented in this skill	Structural-model setup checks, initial SEM fit, global fit reporting, key path reporting, mediation-path labeling
Implemented downstream in vera-data-path-generating	Indirect-effect bootstrapping, multigroup SEM, alternative-path comparison, residual review, manuscript assembly
Out of scope in this open-source baseline	Longitudinal growth SEM and latent-model families not explicitly named above

Minimal Smoke Test

• Smoke-test prompt: "Run vera-data-path-reviewing on a small mediation-style SEM with two latent factors and one structural path chain. Produce the standard baseline artifacts and recommendation block."

Next step: Invoke vera-data-path-generating from this skillset to run the full pipeline (indirect-effect bootstrap CIs, multi-group SEM, alternative-path comparison, manuscript generation). See ../../CROSS-SKILL-INTERFACE.md for the shared handoff contract.

Failure Modes

• If model does not converge: report non-convergence, suggest simplifying structure
• If outcome type is CFA-only (no structural paths): redirect to vera-data-cfa-reviewing
• If longitudinal growth structure detected: redirect to vera-data-growth-reviewing