EVAL 6970: Meta Analysis


This course in the interdisciplinary Ph.D. in evaluation program at Western Michigan University is an advanced graduate seminar designed to provide students with the knowledge, skills and abilities necessary to conduct basic research reviews, research syntheses and meta-analyses. Topics covered include, but are not limited to:

  • The increasing use of meta-analysis in formulating and enacting evidence-based policies and practices.
  • The role of meta-analysis in theory development.
  • Principles and procedures for planning and executing research reviews and meta-analyses
  • Identifying and retrieving literature.
  • Coding studies.
  • Computing effect sizes (e.g., based on means, binary data, and correlations) and their corresponding confidence intervals for meta-analysis.
  • Converting among effect sizes.
  • Factors that affect precision (e.g., variance, standard error, confidence intervals).
  • Fixed-effect and random-effects models for meta-analysis.
  • Identifying and quantifying heterogeneity.
  • Prediction intervals.
  • Subgroup analysis.
  • Meta-regression.
  • Meta-analysis with complex data structures.
  • Power analysis for meta-analysis.
  • Publication bias.
  • Psychometric meta-analysis.

Students should have at least a fundamental knowledge of applied statistics and experimental and quasi-experimental design to succeed in the course and will be required to plan and execute a basic meta-analysis. EMR 6550: Experimental and Quasi-Experimental Designs, is a recommended, but not required, prerequisite.




Dr. Chris L. S. Coryn

Required textbooks

  • Bornenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. West Sussex, UK: Wiley.
  • Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2009). The handbook of research synthesis and meta-analysis (2nded.). New York, NY: Russell Sage Foundation.
  • Comprehensive Meta-Analysis 2.0

Order form

Required readings

These readings are for instructional purposes only.

Data sets and supplementary materials

Homework and projects

Lecture notes

Meta-analysis repositories