Within PhD projects, the performance of a meta-analytic study can be very valuable.
- Target group
- PhD candidate
- Elise Dusseldorp (Associate Professor) Mariëlle Linting (Associate Professor) Ralph Rippe (Assistant Professor)
- Study material
- Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Chichester: Wiley & Sons. - Several articles to be announced.
Two important learning goals for PhD candidates are:
- to obtain a good overview of the state-of-the art of the research field of the PhD-project, and
- to get insight in the way the research problem is tackled, analyzed and reported.
In this 2-day course, the core issues of meta-analysis are explained and practiced.
The first day
The first day of the course is targeted at the basic concepts (measures of effect size, heterogeneity, publication bias, trim and fill method, etc.). Pitfalls in the data collection and coding will also be tackled (e.g., problems with retrieving studies, analyzing multiple outcome measures, multiple treatment arms, multiple follow-up moments, different measures etc.). In addition, the participants perform basic meta-analyses in the programmes R (R-package metafor; Viechtbauer, 2002) and/or Comprehensive Meta-Analysis (Borenstein, 2009) on example data sets.
The second day
The second day is targeted at more advanced aspects of meta-analysis (meta-regression with multiple moderators, interactions between moderators, meta-CART) and new developments in meta-analysis. Each day consists of a mixture of lectures and hands-on exercises.
After this course, you are able to
- understand and explain the basic concepts of meta-analysis: measures of effect-size, fixed-effect and random-effects models, (methods to assess) publication bias, forest plot.
- understand and explain more advanced topics: methods to assess and explain effect size heterogeneity, meta-regression, and meta-CART.
- perform in R and/or CMA: basic meta-analysis, meta-regression, subgroup analysis, publication bias analysis, forest plot, and meta-CART.
- basic knowledge of R (e.g., first lessons of online course Coursera: “Statistics with R”) or basic knowledge of CMA.
- bring your own laptop with R (and R-studio) and/or CMA.
- those who are more experienced in meta-analysis may skip the first day of this course. If in doubt, please contact the coordinator.