Enhance your understanding of meta-analysis. Learn about raw mean differences—specifically for experimental and comparison groups—and how to convert useful outcome measures such as relative risk and odds ratios to commensurate measures of effect size.
- [Conrad] Hello, my name is Conrad Carlberg. Welcome to my course on meta-analysis. In this course, I'll start by walking you through the basic idea of meta-analysis, the history behind it, the reason it's needed, and the principal rational for effect sizes. Next, we'll take a look at raw mean differences, specifically for experimental and comparison groups, then we'll explore binary outcomes in the context of risks and odds ratios. I'll show you a case in which it may be useful to use logarithms as well.
We'll wrap up with an acquiration of confidence intervals, focusing on how they're created for binary outcome measures, and then on how to use them to bracket a mean effect size built on binary data. I hope you're excited to get going with this course so let's get started.
- Rationale for meta-analysis
- Straightforward effect sizes
- Standardized mean differences
- Correlation coefficients
- Complex effect sizes: Risk ratios and odds ratios
- Confidence intervals in meta-analysis
- Building confidence intervals around binary-outcome effect sizes