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.
Preamble After tremendous help from Henrik Singmann and Mattan Ben-Shachar, I finally have two (!) workflows for conditional logistic models with brms. These workflows are on track to make it into the next update of my ebook translation of Kruschke’s text (see here).
What? If you’re in the know, you know there are three major ways to handle missing data:
full-information maximum likelihood, multiple imputation, and one-step full-luxury1 Bayesian imputation. If you’re a frequentist, you only have the first two options.
Preamble Suppose you’ve got data from a randomized controlled trial (RCT) where participants received either treatment or control. Further suppose you only collected data at two time points, pre- and post-treatment.
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?