vera-data-growth-reviewing

name	vera-data-growth-reviewing
description	Longitudinal SEM skill for latent growth and change. Collects wave structure, checks whether a growth/change model is identified, fits an initial linear growth or latent change specification, and reports fit plus trajectory parameters with a recommendation block. Trigger when the user asks for latent growth models, growth curves in SEM, latent change score models, longitudinal latent trajectories, growth curve modeling, LGM, LCSM, trajectory analysis, or change over time in SEM. Does not handle cross- sectional CFA (use vera-data-cfa-reviewing) or non-longitudinal structural models (use vera-data-path-reviewing).
allowed-tools	Read, Bash, Write, Edit

Longitudinal Change Testing — Initial Growth / Change Fit

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 goal is a latent growth or latent change baseline for repeated measurements across waves.
• A first-pass longitudinal SEM checkpoint is needed before nonlinear or multigroup extensions.

Do not use this skill when:

• The data are cross-sectional CFA or non-longitudinal SEM; use the corresponding SEM skill.
• The design is a high-frequency observed time series rather than longitudinal SEM.

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 longitudinal SEM summary
Diagnose	Check Longitudinal Setup	Main Agent	workflow/step02-check-longitudinal-setup.md	Prior step output	Wave/identification decision
Test	Run Primary Model	Main Agent	workflow/step03-run-primary-model.md	Prior step output	Initial LGM/LCSM fit + recommendation

Decision Tree

1. CHECK TIME STRUCTURE
   ├── 3+ waves of same construct → latent growth candidate
   ├── adjacent change focus → latent change score candidate
   └── only 2 waves → limited change model; report cautiously

2. CHECK MEASUREMENT
   ├── repeated observed score only → basic growth/change SEM
   └── latent construct per wave → recommend invariance-aware model

3. CHECK TRAJECTORY FORM
   ├── theory supports linear change → linear growth first
   └── theory suggests curvature → recommend nonlinear/basis model

Required Inputs

Role	What to collect
Repeated measure	Variable name measured at each wave
Time points	Number of waves, spacing (equal/unequal), time coding
Model type	Latent growth (LGM) vs latent change score (LCSM)
Scale type	Continuous, ordinal/Likert indicators per wave
Time-invariant covariates	Optional predictors of intercept/slope
Sample size	Final analytic N, attrition pattern

Code Structure

PART 0: Setup & Data Loading (wide or long format)
PART 1: Longitudinal Setup Checks (waves, identification, estimator)
PART 2: Primary Growth/Change Model Fit (intercept, slope, fit indices)
PART 3: Recommendation Block

Reporting Standards

1. Always report chi-square, df, CFI, TLI, RMSEA, and SRMR
2. Report mean and variance of intercept and slope factors
3. Report intercept-slope covariance/correlation
4. If covariates included: report their effects on intercept and slope
5. Only 2 waves: state limitation explicitly ("minimal identification")
6. Ends with recommendation block for nonlinear growth, piecewise models, time-varying covariates, and class-based trajectories
7. Fit-index thresholds: CFI / TLI ≥ 0.90 (≥ 0.95 preferred); RMSEA ≤ 0.08 (≤ 0.06 preferred); SRMR ≤ 0.08. For ordinal indicators with DWLS/WLSMV, report robust variants.
8. Measurement invariance (if repeated latent constructs across waves): test the standard sequence configural → metric (equal loadings) → scalar (equal intercepts) → strict (equal residual variances). Report ΔCFI < 0.010 and ΔRMSEA < 0.015 as thresholds for invariance at each step. Halt invariance comparison at the first level that fails and note the partial-invariance status.

Method Status

Status	Methods
Implemented in this skill	Wave-structure checks, initial LGM / LCSM fit, intercept / slope summaries, invariance-aware recommendation triggers
Implemented downstream in vera-data-growth-generating	Nonlinear trajectories, latent-basis and change-score variants, multigroup growth, parallel-process extensions, manuscript assembly
Out of scope in this open-source baseline	Cross-sectional SEM, CFA-only work, and longitudinal families not explicitly named above

Minimal Smoke Test

• Smoke-test prompt: "Run vera-data-growth-reviewing on a 3-wave repeated-measure example with equal spacing and continuous indicators. Produce the standard baseline artifacts and recommendation block."

Next step: Invoke vera-data-growth-generating from this skillset to run the full pipeline (nonlinear trajectories, latent-basis/change-score variants, multigroup growth, parallel-process models, manuscript generation). See ../../CROSS-SKILL-INTERFACE.md for the shared handoff contract.

Failure Modes

• If only 2 waves: fit basic change model but warn about minimal identification
• If model does not converge: suggest simpler specification (intercept-only)
• If cross-sectional CFA structure detected: redirect to vera-data-cfa-reviewing
• If non-longitudinal structural model: redirect to vera-data-path-reviewing