In this course, you will be introduced to the most important methods for synthesizing research to estimate the size and variability of an effect. We will cover traditional methods to be applied after data collection, as well as more recent methodological developments to design studies that are informative for the distribution of effect sizes.
The course starts by discussing the purpose, advantages, and disadvantages of different methodologies for synthesizing research. In particular, we will contrast meta-analyses and meta-studies. We then dive deeper into meta-analyses, learning the most important methods for building the dataset and conducting the analysis for estimating effect size, publication bias, and heterogeneity. We practice the analysis in R using open datasets.
Next, we focus on single-paper meta-analysis, a debated but useful method to summarize and communicate the generated evidence in a single research project. The method has generated recent debate that we will discuss in class.
After we have learned and discussed techniques to synthesize research post data collection, we dive deeper into a more recently developed method: meta-studies. Meta-studies are studies designed to understand the entire distribution of effects, their heterogeneity, and moderators. We discuss how meta-studies can complement meta-analyses and learn how to plan and analyze the generated data. We discuss and learn how to apply approaches such as stimulus sampling and other ways of purposive variation.
Alongside the course, students plan their own research project, applying one of the techniques they have learned. They are asked to present their research plan on the last day of the course. Their elaborated research plan, submitted after the course, will be used to determine their grade
