Course by tag

Modeling the Dynamics of Intensive Longitudinal Data

NOTE: this course is fully booked! New applicants will be placed on a waiting list.

This is a four-day course on how to study dynamics in intensive longitudinal data, such as ambulatory assessments (AA), experience sampling method (ESM) data, ecological momentary assessments (EMA), real time data capture, observational data or electronic daily diaries. We provide a tour of diverse modeling approaches for such data and the philosophies behind them, as well as practical experience with these modeling techniques using different software packages (including R and Mplus).

A Gentle Introduction to Bayesian Statistics

This course introduces all the essential ingredients needed to start Bayesian inference or model selection. We discuss specifying priors, obtaining the posterior, prior/posterior predictive checking, and sensitivity analyses. We also discuss evaluating hypotheses via the Bayes Factor, using information criteria and aggregating evidence from multiple studies. We propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics). We have prepared many exercises in R (brms, blavaan, rjags, rstan, rstanarm, bayesreg, restrictor, bain) to get hands-on experience.