Boost your power with baseline covariates

This is the first post in a series on causal inference. Our ultimate goal is to learn how to analyze data from true experiments, such as RCT’s, with various likelihoods from the generalized linear model (GLM), and with techniques from the contemporary causal inference literature. In this post, we review how baseline covariates help us compare our experimental conditions.

Example power analysis report, II

In an earlier post, I gave an example of what a power analysis report could look like for a multilevel model. At my day job, I was recently asked for a rush-job power analysis that required a multilevel model of a different kind and it seemed like a good opportunity to share another example.

Example power analysis report

If you plan to analyze your data with anything more complicated than a t-test, the power analysis phase gets tricky. I’m willing to bet that most applied researchers have never done a power analysis for a multilevel model and probably have never seen what one might look like, either. The purpose of this post is to give a real-world example of just such an analysis.