# New Just-in-Time Workshop for Self-Paced Learning on Structural Equation Modeling

In May of 2020, Dan Bauer and Patrick Curran offered a free three-day Livestream course titled Introduction to Structural Equation Modeling. There were 3,000 participants from 38 countries and six continents. Because many people were unable to register at the time, we are now offering the recordings and materials from this three-day class for just \$49 (simply to cover infrastructure costs) within our new online statistics training platform. In the short video below, Dan and Patrick describe the Just-in-Time Introduction to Structural Equation Modeling workshop.

## My advisor told me I should group-mean center my predictors in my multilevel model because it might “make my effects significant” but this doesn’t seem right to me. What exactly is involved in centering predictors within the multilevel model?

How to specify multilevel models to obtain within- and between-group effects through centering lower-level predictors.

## What exactly qualifies as intensive longitudinal data and why am I not able to use more traditional growth models to study stability and change over time?

This post considers the unique features of intensive longitudinal data (ILD) relative to other more traditional data structures and how we can appropriately analyze ILD given these features

## What’s the best way to determine the number of latent classes in a finite mixture analysis?

Selecting the number of classes (or components) is one of the most challenging decisions to make when fitting a finite mixture model (including latent class analysis and latent profile analysis). In this post, we talk through the conventional wisdom on class enumeration, as well as when this breaks down.

## My advisor told me to use principal components analysis to examine the structure of my items and compute scale scores, but I was taught not to use it because it is not a “true” factor analysis. Help!

We explain the difference between principal components analysis and exploratory factor analysis

## I fit a multilevel model and got the warning message “G Matrix is Non-Positive Definite.” What does this mean and what should I do about it?

Received the cryptic warning message “G matrix is non-positive definite”? Learn what this means and what to do about it.