# What is Growth Curve Modeling?

As Patrick describes in the first of a series of videos, growth curve models can be useful whenever there is a focus on the analysis of change over time, such as when examining developmental changes, evaluating treatment effects, or analyzing diary data. Although growth models go by a variety of different names, all of these approaches share a common focus on the estimation of individual differences in within-person change over time. Growth curve models estimate smoothed trajectories that are unique to each individual based on the set of observed repeated measures. This results in a collection of individual-specific trajectories that then become the unit of analysis, allowing us to ask such questions as: What is the average trajectory? How much do individual trajectories differ from one another? Can we predict these differences as a function of other individual characteristics?

To see all episodes in this series, see our Growth Modeling playlist.

## 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.

## 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

## 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.