What? If you’d like to study the effectiveness of a clinical treatment, one of the simplest and most widely used approaches it to
recruit participants from the target population, measure the outcome variable during a pre-treatment assessment, randomly assign participants into a control condition or an experimental treatment condition, treat the participants in the treatment condition, and measure the outcome variable again at the conclusion of treatment.
Preamble 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.
What? One of Tristan Mahr’s recent Twitter threads almost broke my brain.
wait when people talk about treating overdispersion by using random effects, they sometimes put a random intercept on each row?
Context Someone recently posted a thread on the Stan forums asking how one might make item-characteristic curve (ICC) and item-information curve (IIC) plots for an item-response theory (IRT) model fit with brms.
Orientation This post is the second and final installment of a two-part series. In the first post, we explored how one might compute an effect size for two-group experimental data with only \(2\) time points.
Background This post is the first installment of a two-part series. The impetus is a project at work. A colleague had longitudinal data for participants in two experimental groups, which they examined with a multilevel growth model of the kind we’ll explore in the next post.