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EVAL 6970: Meta Analysis

Description

This course in the interdisciplinary Ph.D. in evaluation program at Â鶹´«Ã½ is an advanced graduate seminar designed to provide Â鶹´«Ã½s 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.

Syllabus

Syllabus

Instructor

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