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
Say you have 2-timepoint RCT, where participants received either treatment or control. Even in the best of scenarios, you’ll probably have some dropout in those post-treatment data. To get the full benefit of your data, you can use one-step Bayesian imputation when you compute your effect sizes. In this post, I’ll show you how.
It turns out that you can use random effects on cross-sectional count data. Yes, that’s right. Each count gets its own random effect. Some people call this observation-level random effects and it can be a tricky way to handle overdispersion. The purpose of this post is to show how to do this and to try to make sense of what it even means.
If you plan to analyze your data with anything more complicated than a t-test, the power analysis phase gets tricky. I’m willing to bet that most applied researchers have never done a power analysis for a multilevel model and probably have never seen what one might look like, either. The purpose of this post is to give a real-world example of just such an analysis.
The purpose of this blog post is to show how one might make ICC and IIC plots for brms IRT models using general-purpose data wrangling steps.
When your MCMC chains look a mess, you might have to manually set your initial values. If you’re a fancy pants, you can use a custom function.
Just this past week, I learned that, Yes, you can fit an exploratory factor analysis (EFA) with lavaan. The purpose of this blog post is to make EFAs with lavaan even more accessible and web searchable by walking through a quick example.
The purpose of this post is to highlight some of the steps I took to rebuild my academic-style blogdown website. At a minimum, I’m hoping this post will help me better understand how to set up my website the next time it needs an overhaul. Perhaps it will be of some help to you, too.
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).