Applied Systematic Review and Meta-Analysis

“Non-reproducible single occurrences are of no significance to Science.” - Popper (1956).
With this quotation, Karl Popper already named in 1956 a highly relevant issue in science - the replicability of scientific studies. The increasing amount of literature makes it difficult for researchers to assess and keep up with new evidence. This is exactly where evidence-based methods, such as systematic reviews and meta-analyses, come into play. In a nutshell, systematic reviews and meta-analyses can be described as a set of methods for aggregating, summarizing, and drawing inferences from collections of thematically-related studies. The key idea is to describe the results and different study design features qualitatively and/or quantitatively.

The aim of this five-day course is to provide participants with an applied introduction to methods for conducting systematic reviews and meta-analyses in the social sciences. This course provides a step-by-step approach and consists of the following:
(I) formulating a research question;
(II) defining the eligibility criteria for including and excluding studies;
(III) conducting the literature search and screening studies (also with the help of text mining methods);
(IV) coding studies and effect sizes;
(V) synthesizing evidence; and,
(VI) presenting and interpreting the results.

The course will be interactive and practical, with the delivery of sessions based on lectures and individual/small groups working on their own research projects. Through several exercises, participants will have ample opportunity to directly apply what they have learned in each of the six steps mentioned above onto their own projects. Prior use of R is advantageous but not a prerequisite for this course.

Course leader

Jessica Daikeler is a survey methodologist and works in the Survey Operations team at GESIS Leibniz Institute for the Social Sciences in Mannheim, Germany.
Sonila Dardha is a survey methodologist – quantitative UX researcher at Meta in London, UK.

Target group

Participants will find the course useful if:
- they are planning to or currently working on a Master/PhD thesis, or a scientific publication in social sciences or survey methodology using research synthesis methods (systematic review or meta-analysis).
- they want to gain a better understanding of the pros and cons of the method when evaluating meta-analytical results. However, in this course participants are encouraged to work on their own projects and perform all the different steps of systematically synthesising evidence for their research problem.

Prerequisites
Participants are …
- expected to have a forthcoming/working project that necessitates knowledge in systematic reviews or meta-analyses.
- expected to have a good working knowledge of statistics at an undergraduate level, e.g., statistical inference (standard error, confidence interval), bivariate statistics (correlation coefficient, mean differences, odds ratio) as well as a basic understanding of (linear) regression analysis and ANOVA.
- interested in any sub-topic in social research or survey methodology.
- expected to install R, and preferably RStudio, as well as the metafor package on their computers.

Course aim

By the end of the course participants will be able to:
- define a review question and understand how to develop a review protocol and the key stages of the systematic review process.
- develop a search strategy to identify relevant studies for a specific review question.
- understand how to conduct a comprehensive literature search.
- apply eligibility criteria to identify relevant studies.
- learn how to use text mining methods to conduct more effective literature search.
- understand how to perform a meta-analysis and how to present meta-analytic results.
- conduct a basic meta-analysis and meta-regression using the metafor package in R.

Credits info

4 EC
- Certificate of attendance issued upon completion.

Optional bookings:
The University of Mannheim acknowledges the workload for regular attendance, satisfactory work on daily assignments and for submitting a paper of 5000 words to the lecturer(s) by 15 October at the latest with 4 ECTS (70 EUR administration fee).

Fee info

EUR 500: Student/PhD student rate.
EUR 750: Academic/non-profit rate.
The rates include the tuition fee, course materials, the academic program, access to library and IT facilities, and coffee/tea breaks.