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?
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.
Purpose Just this past week, I learned that, Yes, you can fit an exploratory factor analysis (EFA) with lavaan (Rosseel, 2012; Rosseel & Jorgensen, 2019). At the moment, this functionality is only unofficially supported, which is likely why many don’t know about it, yet.
Purpose A few weeks ago, I was preparing to release the second blog post in a two-part series (you can find that post here). During the editing process, I had rendered the files into HTML and tried posting the draft to my website.
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.