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.
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.
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.