longitudinal

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.

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.