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Meta-analysis (RSS2.17) - Closed

Learn to perform cutting-edge meta-analysis using free, open-source software (in R). Basics topics include statistical models, effect sizes, forest plots, summary effects and heterogeneity estimates. Advanced topics include three-level meta-analysis for dependent data, exploring heterogeneity using machine learning, publication bias, computational reproducibility and open science. Requires undergraduate statistics knowledge; the focus is on conceptual understanding. Does not cover systematic review.

Duration: one-week.

    General

     

    This course is closed, registration is no longer possible. 

    This course teaches you to conduct meta-analyses according to contemporary best practices, using only free open-source software. It covers the basics of meta-analysis, including statistical models for meta-analysis, calculating, pooling, and converting effect sizes, visualizing the distribution of observed effect sizes using forest plots, and estimating and interpreting summary effect sizes. Particular attention is devoted to quantifying and explaining heterogeneity in effect sizes, a common challenge in applied meta-analyses. What sets this workshop apart are the advanced topics that address prevalent challenges in meta-analysis. We cover methods for testing and mitigating publication bias. 

    Furthermore, the course introduces the concept of three-level meta-analysis, a powerful tool for dealing with dependent data, which occurs when coding multiple effect sizes from the same paper or sample. In addition, the course introduces cutting-edge machine learning methods for exploring heterogeneity in effect sizes, including random forest meta-regression and Bayesian regularized meta-regression. 

    Finally, the course addresses computational reproducibility and open science in meta-analysis. The workshop is designed to be accessible for researchers with a foundational understanding of multiple linear regression (undergraduate level). Statistical concepts are explained at a conceptual level, rather than a mathematical level, and applied examples and tutorials are used to build a working knowledge of the methods. Those with prior experience in meta-analysis might enjoy the focus on open source software and advanced techniques, including machine learning methods. 

    The course does not cover the systematic literature search. All analyses are performed in R, predominantly using the packages metafor, metaforest, and pema.

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    Starting date

    24 June 2024, 9 am
    Educational method
    On-site
    Main Language
    English
    Sessions
    24 June 2024, 9 am - 28 June 2024, 5 pm
    Teacher(s)
    Caspar van Lissa
    Unique code
    RSS2.17

    Factsheet

    Type of education
    Course
    Entry requirements
    See the requirements in cost and admission
    Study load (ECTS)
    2
    Result
    Certificate
    Organisation
    Radboud Summer School

    Total package & social events

    Caspar van Lissa

    Caspar van Lissa
     

    Dr. Van Lissa is associate professor of social data science at Tilburg University and associate editor of "Research Synthesis Methods". His research is on machine learning-informed methods to account for heterogeneity in meta-analyses, and text mining methods to qualitatively summarize published literature. He is chair of the Open Science Community Tilburg and publishes on open reproducible code in research.

    This course is closed, registration is no longer possible. 

     

    Costs

    • Regular: €1049 (application deadline 1st of May)
    • Student & PhD's: €699 (application deadline 1st of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships

    Admission

    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: 

    A thorough understanding of multiple linear regression (at least at undergraduate level) and passing familiarity with R, or willingness to learn R. Optionally: multilevel modeling, Bayesian statistics, machine learning/data science, systematic search and review.

    Admission documents: 

    None