# Education Meta Analysis Preparation

> Use for education meta-analysis papers or systematic reviews that quantitatively synthesize effect sizes. Supports effect-size extraction, coding sheet design, heterogeneity, moderator analysis, publication bias, forest/funnel plots, and R metafor/meta workflows.

- Skill: `ilog3/education-meta-analysis-preparation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ilog3/education-meta-analysis-preparation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ilog3/education-meta-analysis-preparation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ilog3 (https://skillmd.com/u/ilog3)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/ilog3/education-meta-analysis-preparation

---


# Education Meta-Analysis Preparation

## Goal

Prepare data structures and analysis plans for a defensible education meta-analysis.

## Inputs

- Systematic review question
- Eligible intervention/effect studies
- Outcome types and statistics reported
- Study designs
- Moderator candidates

## Workflow

1. Confirm enough comparable quantitative studies exist.
2. Define effect size metric: SMD/Hedges g, correlation, odds ratio, risk ratio.
3. Create coding manual and extraction sheet.
4. Plan dependency handling for multiple effects.
5. Choose random/fixed effects model.
6. Plan heterogeneity, moderators, sensitivity, publication bias.
7. Produce analysis script skeleton and reporting template.

## Tool Calls

R packages:

```r
install.packages(c("metafor", "meta", "dmetar", "clubSandwich", "robumeta"))
```

RevMan:

```text
https://training.cochrane.org/online-learning/core-software/revman
```

Extraction sheet:

```text
Excel/Sheets with study_id, effect_id, n_treat, n_control, mean/sd, outcome, timepoint, moderator fields
```

## Output Format

| Field | Definition | Coding Rule |
|---|---|---|

Include:

- Effect size plan
- Coding sheet
- R analysis outline
- Forest/funnel plot plan
- Reporting checklist

## Quality Rules

- Meta-analysis requires comparable outcomes and enough studies.
- Random effects are common in education due to contextual heterogeneity.
- Record decisions for dependent effect sizes.