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).
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?
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.
Purpose In the contemporary longitudinal data analysis literature, 2-timepoint data (a.k.a. pre/post data) get a bad wrap. Singer and Willett (2003, p. 10) described 2-timepoint data as only “marginally better” than cross-sectional data and Rogosa et al.
The set-up PhD candidate Huaiyu Liu recently reached out with a question about how to analyze clustered data. Liu’s basic setup was an experiment with four conditions. The dependent variable was binary, where success = 1, fail = 0.