# This Week on CenterStat Unscripted: Exploratory & Confirmatory Factor Analysis

This Thursday at noon (3/2, 12:00 pm ET), Dan and Patrick introduce the basics of factor analysis, both exploratory and confirmatory, and describe potential advantages and disadvantages to each. They begin by discussing traditional factor analytic methods and then explore more recent developments in measurement and scoring. The conclude with how these methods might be best used in practice and make recommendations for further training. Patrick might or might not take the opportunity to lob more non-dairy creamer containers at Dan.

Please join in on the fun at https://www.youtube.com/@centerstat/streams, or by following the link on the Unscripted webpage at https://centerstat.org/unscripted/.

For those who can’t catch the live broadcasts, a recording of each episode is posted to the CenterStat YouTube Channel. We can’t promise the recordings will be any more entertaining, but at least you can watch them at double-speed.

## Announcing CenterStat Unscripted: A New Weekly YouTube Livestream on Quantitative Methods

In large part because Dan and Patrick have jobs from which they can’t be fired, they keep coming up with random activities with which to…

## 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.

## This Week on CenterStat Unscripted: An Introduction to the Latent Curve Model

This Thursday at noon (2/23, 12:00 pm ET), Dan and Patrick will bicker their way through an introduction to the latent curve model (LCM), a…

## 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’m reporting within- and between-group effects in from a multilevel model, and my reviewer says I need to address “sampling error” in the group means. What does this mean, and what can I do to address this?

Why between-group effects estimating in MLMs are sometimes biased, and what to do about it