AI-Powered Meta-Analysis (From Basic to Advance)

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About Course

Are you struggling with the tedious, error-prone manual data extraction required for meta-analysis? Do you want to go beyond simple bivariate meta-analysis and fit complete structural equation models across dozens of published studies? This advanced 4-week masterclass teaches you how to build an AI-driven, automated data collection workflow and combine it with R to execute cutting-edge Meta-Analytic Structural Equation Modeling (MASEM/MSEM). By pairing Artificial Intelligence with R packages like `metafor` and `metaSEM`, you will automate effect size extraction from PDFs, pool correlation matrices, and test complex theoretical frameworks for top-tier academic journals.

What Will You Learn?

  • Automate data extraction from research PDFs (effect sizes, sample sizes, t-values) using specialized AI automation.
  • Convert raw study outputs into standardized effect sizes r, d, Fisher's z automatically.
  • - Generate and debug reproducible R code for Meta-Analysis using LLMs as your personal coding assistant.
  • - Master Random-Effects ; Fixed-Effects models, quantifying heterogeneity I2, Q, and subgroup meta-regressions.
  • - Conduct publication bias diagnostics (Funnel Plots, Egger's Test, Trim; Fill).
  • - Master Stage 1 ; Stage 2 Meta-Analytic Structural Equation Modeling (MASEM) in R to test path models and mediation.
  • - Draft publication-ready PRISMA-MA compliant manuscripts with automated visualizations.

Course Content

Module 1: AI-Automated Data Extraction & Effect-Size Standardization

Module 2: R-Powered Univariate Meta-Analysis with AI

Module 3: Advanced Meta-Analytic Structural Equation Modeling (MASEM / MSEM)

Module 4: Advanced Extensions, Reporting, and Paper Writing for High-Impact Journals

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