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
This post is the second 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. In this second post, we fulfill our goal to show how to generalize this framework to experimental data collected over 3+ time points. The data and overall framework come from Feingold (2009).
The purpose of this series is to show how to compute a Cohen’s-d type effect size when you have longitudinal data on 3+ time points for two experimental groups. In this first post, we’ll warm up with the basics. In the second post, we’ll get down to business. The data and overall framework come from Feingold (2009).
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.
PhD candidate Huaiyu Liu recently reached out with a question about how to analyze clustered data. Liu’s basic setup was an experiment with four conditions. The dependent variable was binary, where success = 1, fail = 0. Each participant completed multiple trials under each of the four conditions. The catch was Liu wanted to model those four conditions with a multilevel model using the index-variable approach McElreath advocated for in the second edition of his text. Like any good question, this one got my gears turning. Thanks, Liu! The purpose of this post will be to show how to model data like this 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.