Meta-Analysis: Introduction and Application


Meta-analysis is used to: combine evidence across different research studies; integrate multiple studies into a single statistical framework; yield more precise estimates of effect sizes; allow for unique treatment comparisons; explain differences arising from conflicting study results; and identify areas for future research.

Meta-analytic methods are employed in a diverse array of disciplines across the social, behavioral, health, and agricultural sciences.

This workshop provides hands-on exposure to the process involved in conducting a meta-analysis from the planning stage, through the selection of appropriate statistical techniques, through the issues involved in analyzing data (frequentist and Bayesian methods), to the interpretation of results. Examples and case studies from the biological and social sciences will be integrated into the discussions and lectures.

Prerequisites: The course assumes a basic knowledge of statistics and linear regression analysis.

Software: In-class examples will rely mostly on the R Statistical Computing Environment. However, the techniques covered in class are also available and easily accessible in other major software packages (e.g., Stata, SAS, and SPSS). Participants who need to brush up on their statistical software knowledge are encouraged to take the "Introduction to the R Statistical Computing Environment" lectures or the "Introduction to Computing" lectures.

Fees: Consult the fee structure.

Tags: meta-analysis

Course Sections