# multilevel

## Just use multilevel models for your pre/post RCT data

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.

## Example power analysis report, II

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.

## Notes on the Bayesian cumulative probit

What/why? Prompted by a couple of my research projects, I’ve been fitting a lot of ordinal models, lately. Because of its nice interpretive properties, I’m fond of using the cumulative probit.

## Use emmeans() to include 95% CIs around your lme4-based fitted lines

Scenario You’re an R (R Core Team, 2020) user and just fit a nice multilevel model to some grouped data and you’d like to showcase the results in a plot.

## Got overdispersion? Try observation-level random effects with the Poisson-lognormal mixture

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?

## Example power analysis report

Context In one of my recent Twitter posts, I got pissy and complained about a vague power-analysis statement I saw while reviewing a manuscript submitted to a scientific journal.

## Make ICC plots for your brms IRT models

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.