# What’s the difference between finite mixture models and cluster analysis?

Researchers are often interested in identifying subgroups within their data to better understand heterogeneity within the population under study. This task has been the traditional domain of cluster analysis, but over the past decade or so finite mixture models have become an increasingly preferred alternative analytic technique. Sometimes referred to as “model-based clustering” the finite mixture model differs in important ways from more classical methods of cluster analysis, such as the K-Means algorithm. In this video, Dan provides an intuitive description of the underlying assumptions and purposes of the finite mixture model as contrasted with K-means clustering. He describes several important differences between finite mixture models and other cluster analysis techniques that might motivate applied researchers to select one approach over another. If you are interested in learning more about these techniques, including their implementation in popular software programs, you may wish to consider enrolling in our 5-day summer workshop on Cluster Analysis and Mixture Modeling.

## 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.

## 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

## 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

## 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.

## 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.