Statistical Methods for Meta-Analysis

Synthesizing multiple research studies to find a definitive conclusion is one of the most powerful skills in modern data science and academic research. This text-based course guides you from foundational statistical concepts to performing and interpreting your own meta-analyses. You will learn how to systematically combine quantitative findings, evaluate study quality, and address variance across different research designs. By studying clear written explanations and step-by-step statistical walkthroughs, you will develop the practical skills needed to conduct rigorous research synthesis. What you'll learn: 1. Understand the core principles of systematic reviews and effect size calculations. 2. Apply fixed-effect and random-effects models to pool diverse study results. 3. Analyze heterogeneity across studies using modern statistical diagnostics. 4. Evaluate publication bias using funnel plots and advanced correction methods. 5. Practice interpreting and reporting meta-analytic findings using open science standards. The course begins with essential terminology, basic data structures, and foundational definitions before moving into practical calculation models and bias detection. You will work through structured written examples and conceptual exercises that reinforce each statistical method. This course is designed for researchers, data analysts, and students who want to build a strong foundation in research synthesis. No prior advanced statistical background is required. Start mastering the science of research synthesis today.