meta-analysis-fundamentals

Explain meta-analysis concepts, effect sizes, and fixed versus random effects.

1|Updated Dec 29, 2025

Search Tags:#meta-analysis #research-methods #evidence-synthesis #effect-size #fixed-effects #random-effects #statistics-education

Ready to run

npx skills add https://github.com/matheus-rech/meta-agent-mobile --skill meta-analysis-fundamentals

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: meta-analysis-fundamentals
Source: https://github.com/matheus-rech/meta-agent-mobile/tree/main/agentskills/meta-analysis-fundamentals
Command: npx skills add https://github.com/matheus-rech/meta-agent-mobile --skill meta-analysis-fundamentals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Meta-analysis often confuses learners; this skill provides clear explanations and guided learning for evidence synthesis.

Core Features & Use Cases

Clear definitions of effect sizes (OR, RR, SMD, MD) and model choices (fixed vs random).
Step-by-step guidance with Socratic prompts to facilitate understanding across languages and regions.
Reference to standard methodologies and key resources.

Quick Start

Explain the foundations of meta-analysis with a concise example comparing binary outcomes.

Frequently Asked Questions about meta-analysis-fundamentals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is the difference between fixed and random effects models in meta-analysis?▼

Fixed effects models assume a single true effect size across all pooled studies, while random effects models account for variation between studies. Meta-analysis guidance helps determine the appropriate model based on whether true effect sizes vary across your evidence synthesis context.

How do I calculate effect sizes for pooling results across studies?▼

Effect sizes for meta-analysis are calculated using metrics like Odds Ratio (OR), Risk Ratio (RR), Standardized Mean Difference (SMD), or Mean Difference (MD). The skill provides clear definitions and step-by-step guidance for applying these effect size calculations to binary or continuous outcomes.

When do I need meta-analysis for evidence synthesis instead of a systematic review?▼

Meta-analysis is needed for evidence synthesis when you want to statistically pool quantitative results across multiple comparable studies rather than summarizing findings qualitatively. It provides a combined effect size estimate to resolve conflicting study outcomes.

What is the best way to learn meta-analysis methodology for research?▼

The best way to learn meta-analysis methodology is through structured explanations, concise examples comparing binary outcomes, and Socratic prompts that test understanding. This skill delivers guided learning on standard methodologies and key resources for evidence synthesis.

Can I use this meta-analysis guidance for different types of research data?▼

Yes, meta-analysis guidance applies to various research data types including binary outcomes and continuous data. It covers core concepts like effect sizes and model choices, facilitating understanding across different languages and regional research contexts.

Why does my meta-analysis show high heterogeneity and what should I do?▼

High heterogeneity in meta-analysis indicates substantial variation in effect sizes across pooled studies, suggesting a random effects model may be more appropriate than fixed effects. The skill explains model choices and references standard methodology to address evidence synthesis limitations.