I mainly post about data analysis and applied statistics stuff, usually in R. Frequent topics include Bayesian statistics, causal inference, multilevel models, and statistical power.
Written by A. Solomon Kurz
In this fourth post, we refit the models from the previous posts with Bayesian software, and show how to compute our primary estimates when working with posterior draws. The content will be very light on theory, and heavy on methods. So if you don’t love that Bayes, you can feel free to skip this one.
In this third post of the causal inference series, we switch to a binary outcome variable. As we will see, some of the nice qualities from the OLS paradigm fall apart when we want to make causal inferences with binomial models.
In this second post, we learn how the potential outcomes framework can help us connect our regression models to estimands from the contemporary causal inference literature. We start with simple OLS-based models. In future posts, we’ll expand to other models from the GLM.
This is the first post in a series on causal inference. Our ultimate goal is to learn how to analyze data from true experiments, such as RCT’s, with various likelihoods from the generalized linear model (GLM), and with techniques from the contemporary causal inference literature. In this post, we review how baseline covariates help us compare our experimental conditions.
The purpose of this post is to highlight some of the steps I took to switch my blogdown website to the Hugo Apéro theme. 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.
In this post, we discuss ways to set a prior for sigma when you know little about your sum-score data. Along the way, we intruduce Popoviciu’s inequality, the uniform distribution, and the beta-binomial distribution.
In an earlier post, we walked through method for plotting the fitted lines from models fit with multiply-imputed data. In this post, we’ll discuss another neglected topic: How might one compute standardized regression coefficients from models fit with multiply-imputed data?
This is a follow-up to my earlier post, Notes on the Bayesian cumulative probit. This time, the topic we’re addressing is: After you fit a full multilevel Bayesian cumulative probit model of several Likert-type items from a multi-item questionnaire, how can you use the model to compute an effect size in the sum-score metric?
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.