# tidyverse

## If you fit a model with multiply imputed data, you can still plot the line.

What? If you’re in the know, you know there are three major ways to handle missing data: full-information maximum likelihood, multiple imputation, and one-step full-luxury1 Bayesian imputation. If you’re a frequentist, you only have the first two options.

## Sexy up your logistic regression model with logit dotplots

What When you fit a logistic regression model, there are a lot of ways to display the results. One of the least inspiring ways is to report a summary of the coefficients in prose or within a table.

## One-step Bayesian imputation when you have dropout in your RCT

Preamble Suppose you’ve got data from a randomized controlled trial (RCT) where participants received either treatment or control. Further suppose you only collected data at two time points, pre- and post-treatment.

## Got overdispersion? Try observation-level random effects with the Poisson-lognormal mixture

What? One of Tristan Mahr’s recent Twitter threads almost broke my brain. wait when people talk about treating overdispersion by using random effects, they sometimes put a random intercept on each row?

## Example power analysis report

Context In one of my recent Twitter posts, I got pissy and complained about a vague power-analysis statement I saw while reviewing a manuscript submitted to a scientific journal.

## Make ICC plots for your brms IRT models

Context Someone recently posted a thread on the Stan forums asking how one might make item-characteristic curve (ICC) and item-information curve (IIC) plots for an item-response theory (IRT) model fit with brms.

tl;dr 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.

## Effect sizes for experimental trials analyzed with multilevel growth models: Two of two

Orientation This post is the second and final installment 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.

## Effect sizes for experimental trials analyzed with multilevel growth models: One of two

Background This post is the first installment of a two-part series. The impetus is a project at work. A colleague had longitudinal data for participants in two experimental groups, which they examined with a multilevel growth model of the kind we’ll explore in the next post.

## Regression models for 2-timepoint non-experimental data

Purpose In the contemporary longitudinal data analysis literature, 2-timepoint data (a.k.a. pre/post data) get a bad wrap. Singer and Willett (2003, p. 10) described 2-timepoint data as only “marginally better” than cross-sectional data and Rogosa et al.