I mainly post about data analysis and applied statistics stuff, usually in R. Frequent topics include Bayesian statistics, multilevel models, and statistical power.
Written by A. Solomon Kurz
I recently came across Jeffrey Walker’s free text, Elements of statistical modeling for experimental biology, which contains a nice chapter on 2-timepoint experimental designs. Inspired by his work, this post aims to explore how one might analyze non-experimental 2-timepoint data within a regression model paradigm. Turns out you probably have more options than you realized.
PhD candidate Huaiyu Liu recently reached out with a question about how to analyze clustered data. Like any good question, this one got my gears turning. Thanks, Liu! The purpose of this post will be to show how to model clustered data in two different ways.
This is an early draft of my second attempt at explaining the connection between meta-analyses and the Bayesian multilevel model. This time, we focus on odds ratios. Enjoy!
You too can make Kruschke-style model diagrams with the tidyverse and patchwork packages. Here’s how.
When you have a time-varying covariate you’d like to add to a multilevel growth model, it’s important to break that variable into two. One part of the variable will account for within-person variation. The other part will account for between person variation. Keep reading to learn how you might do so when your time-varying covariate is binary.
When people conclude results from group-level data will tell you about individual-level processes, they commit the ecological fallacy. This is true even of the individuals whose data contributed to those group-level results. This phenomenon can seem odd and counterintuitive. Keep reading to improve your intuition.
If you are under the impression group-level data and group-based data analysis will inform you about within-person processes, you would be wrong. Stick around to learn why.
Binary data are a little weird. In this post, we’ll focus on how to perform power simulations when using the binomial likelihood to model binary counts.
Data analysts need more than the Gauss. In this post, we’ll focus on how to perform power simulations when using the Poisson likelihood to model counts.