Bayesian meta-analysis in brms-II
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!
Make model diagrams, Kruschke style
You too can make Kruschke-style model diagrams with the tidyverse and patchwork packages. Here’s how.
Time-varying covariates in longitudinal multilevel models contain state- and trait-level information: This includes binary variables, too
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.
Individuals are not small groups, II: The ecological fallacy
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.
Individuals are not small groups, I: Simpson’s paradox
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.
Bayesian power analysis: Part III.b. What about 0/1 data?
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.
Bayesian power analysis: Part III.a. Counts are special.
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.
Bayesian power analysis: Part II. Some might prefer precision to power
When researchers decide on a sample size for an upcoming project, there are more things to consider than null-hypothesis-oriented power. Bayesian researchers might like to frame their concerns in terms of precision. Stick around to learn what and how.
Bayesian power analysis: Part I. Prepare to reject \(H_0\) with simulation.
If you’d like to learn how to do Bayesian power calculations using brms, stick around for this multi-part blog series. Here with part I, we’ll set the foundation.