Within-person factorial experiments, log(normal) reaction-time data

This is the first post in a new series on causal inference. We will learn how to analyze data from true experiments with techniques from the contemporary causal inference literature. Unlike with my earlier series focused on the generalized linear model (GLM), all the data in this series will have more complicated structures, which we’ll capture with the generalized linear mixed model (GLMM). In this first post, we walk out the general framework using log(normal) reaction-time data collected from a within-person factorial experiment.

Just use multilevel models for your pre/post RCT data

I’ve been thinking a lot about how to analyze pre/post control group designs, lately. Happily, others have thought a lot about this topic, too. The goal of this post is to introduce the change-score and ANCOVA models, introduce their multilevel-model counterparts, and compare their behavior in a couple quick simulation studies. Spoiler alert: The multilevel variant of the ANCOVA model is the winner.

Example power analysis report, II

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.

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

You’re an R user and just fit a nice multilevel model to some grouped data and you’d like to showcase the results in a plot. In your plots, it would be ideal to express the model uncertainty with 95% interval bands. If you’re a frequentist and like using the popular lme4 package, you might be surprised how difficult it is to get those 95% intervals. I recently stumbled upon a solution with the emmeans package, and the purpose of this blog post is to show you how it works.