Meta-analysis allows us to synthesize the results of separate studies. The goal is to assess the mean effect size and also heterogeneity – how much the effect size varies across studies. [Read more…] about Member Training: Heterogeneity in Meta-analysis
They communicate just how big the effects are in your statistical results — something p-values can’t do.
But they’re only useful if you can choose the most appropriate one and if you can interpret it.
This can be hard in even simple statistical tests. But once you get into complicated models, it’s a whole new story. [Read more…] about Member Training: Interpretation of Effect Size Statistics
Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.
As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)
We had so many that although I spent about 40 minutes answering [Read more…] about Your Questions Answered from the Interpreting Regression Coefficients Webinar
Meta-analysis is the quantitative pooling of data from multiple studies. Meta-analysis done well has many strengths, including statistical power, precision in effect size estimates, and providing a summary of individual studies.
- A review of basic concepts of statistical power and effect size
- A simulation-based approach to power analysis
- An overview of how to implement simulations in various popular software programs.
Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe that’s just me.
Regardless of how they make you feel, they are super important to do for your study before you collect the data.