yDiv/HIGRADE course: Meta-analysis in biological and environmental sciences



Course Dates

Students admitted / Credit Points

The registration is closed

iDiv Leipzig, Deutscher Platz 5a, room Symbiosis

23-26 October 2017, 09:00 am - 5:00 pm

15-20 / 1,5 ECTS credits


Meta-analysis is the statistical synthesis of the results of independent research studies. Meta-analysis is firmly established in medical and social sciences research, and has become an important statistical approach in the fields of ecology, evolution, and environmental sciences in recent decades. This course will cover the framework of meta-analysis specifically as it is being used in the biological and environmental sciences. Students will learn how to conduct a meta-analysis from start to finish, including searching the literature, extracting data from primary literature, computing effect sizes, testing for the effects of covariates (moderators of explanatory variables), examining assumptions and sources of bias, and presenting the results. Finally, we will discuss methodological issues, advances, and common mistakes in biological meta-analysis.

After completion of the course, the participants are expected to be able to

  • Define meta-analysis and describe its role in the research process

  • Demonstrate how to assess and interpret variation in effect size across studies

  • List and perform the different steps included in a meta-analysis

  • Compute effect sizes and treatment effects

  • Analyze causes of variation in effect sizes

  • Understand how a meta-analysis works to interpret and evaluate published meta-analyses and conduct own meta-analyses

  • ‘Meta-analytical thinking’ allows scientists to see single primary research papers as essential contributions to a larger picture within a research topic

The course will be taught full-time during 4 days. Each day during the course will include both theoretical lectures and extensive hands-on experience with practical assignments and group projects.

Important: Please bring your laptop with a running R version (http://cran.at.r-project.org/). You will need access to Web of Science (possible via eduroam).


Lecturer in charge

Dr. Dylan Craven



Further lecturers

Dr. Katharina Gerstner



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